Supplementary material - Appendix 1¶
Western blot and cell viability data analyses¶
Stressors and concentrations:¶
Control - No stress¶
Hydrogen peroxide (H2O2) - 100, 300, 500 & 1250 $\mu$M¶
Heat - 50$^\circ$C¶
Cadmium sulfate (CdSO4) - 8 mM¶
Potassium ferricynaide (K3Fe(CN)6) - 30 mM¶
Cell viability assay - MTT Tetrazolium Reduction Assay¶
Contents:¶
Section S1 - Separating oxidized and reduced Trx1 by alkylation using purified Trx1¶
Section S2 - Western blot parameter optimization and control analyses¶
Section S3 - MTT assay cell viability results and analyses¶
Section S4 - Stressor blots and thioredoxin redox charge analyses¶
Section S5 - Thioredoxin redox charge and cell viability correlation plots¶
Section S6 - Total protein (Ponceau S stain) analyses¶
Section S7 - Supporting correlation data between the Trx redox charge and cell viability extrapolated from literature¶
Section S8 - Model of hydrogen peroxide metabolism in fission yeast¶
In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy.stats as sp
import scipy as sp
from scipy import interpolate
from scipy.interpolate import CubicSpline
from pingouin import distance_corr
import scipy.optimize
import scipy.misc
import scipy.stats
import os
%pylab inline
import matplotlib.image as mpimg
from matplotlib.pyplot import figure
backupdir = os.getcwd()
os.chdir(backupdir)
Section S1 - Separating oxidized and reduced Trx1 by alkylation, using purified Trx1¶
Alkylation is a chemical process that involves alkyl groups being added to a substrate molecule. Maleimides like methoxypolyethylene glycol maleimide (PEG-maleimide), 4-acetamido-4′-maleimidylstilbene-2,2′-disulfonic acid (AMS), 4-acetamido-4'-((iodoacetyl)amino)stilbene-2,2'-disulfonic acid (AIS), N-ethyl maleimide (NEM) and iodoacetamide (IAM) are alkylating agents that add alkyl groups to free thiols exposed. The alkylation of free thiol groups present within reduced thioredoxin is what causes a shift in mobility of the protein isoform during gel electrophoresis, allowing for separation of reduced and oxidized thioredoxin.
A pure system was needed to determine the alkylating agent that would best separate the thioredoxin isoforms, therefore Schizosaccharomyces pombe Trx1 was expressed in a pET28α vector in Escherichia coli BL21 cells and purified. Subsequent alkylation experiments were performed.
Figure S1 Recombinant Schizosaccharomyces pombe Trx1 induced with 0.5 mM IPTG¶
In [2]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Induction.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
Figure S2 Ni-NTA purification of His-tagged Trx1¶
In [3]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Imidazole washes.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
Figure S3 Batch Ni-NTA purification of His-tagged Trx1¶
In [4]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Purification.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
Figure S4 Alkylation of recombinant Trx1 using IAM (75 mM), NEM (25 mM) and PEG-maleimide (3 mM)¶
In [5]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Alkylation_1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
Figure S5 Alkylation of recombinant Trx1 using AMS (15 mM) and AIS (25 mM)¶
In [6]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Alkylation_2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
Figure S6 Alkylation of recombinant Trx1 using PEG-maleimide (3 mM)¶
In [7]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Alkylation_3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
Section 2 - Western blot parameter optimization and control analyses¶
Stressor time-course experiments were carried out on Schizosaccharomyces pombe (JB35 cells, FLAG-tagged Trx1) and redox Western blotting analyses were used to analyze the samples.
First, optimization for the Western blotting assay was carried out to determine the optimal sample protein concentration, primary and secondary antibody concentrations and to confirm that the signal detected from the protein concentration chosen was in the linear range of detection.
Figure S7 Bovine serum albumin (BSA) standard curve to determine unknown S. pombe JB35 sample protein concentration¶
In [57]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Standard curve_1.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Standard curve_1.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Standard curve_1.csv')
Standard_curve_1 = pd.read_csv('Standard curve_1.csv')
Standard_curve_1["Corrected_OD_Rep1"] = (Standard_curve_1['Rep_1'] - Standard_curve_1['Blank'])
Standard_curve_1["Corrected_OD_Rep2"] = (Standard_curve_1['Rep_2'] - Standard_curve_1['Blank'])
Standard_curve_1["Corrected_OD_Rep3"] = (Standard_curve_1['Rep_3'] - Standard_curve_1['Blank'])
Standard_curve_1['Average_OD'] = Standard_curve_1.iloc[:, [5,6,7]].mean(axis=1)
Standard_curve_1['Std error of average OD'] = Standard_curve_1.iloc[:, [5,6,7]].sem(axis=1)
Standard_curve_1
Out[57]:
In [93]:
fig, ax1 = plt.subplots(figsize=(9,6))
x = Standard_curve_1['BSA concentration (ug/ml)']
y = Standard_curve_1['Average_OD']
yerror = Standard_curve_1['Std error of average OD']
xerror = Standard_curve_1['Std error of average OD']
#plt.plot(x,y)
plt.errorbar(x,y,yerr=yerror,xerr=xerror, color='black', capsize=3)
ax1.set_ylabel(r'Absorbance (562 nm)', size=20)
ax1.set_xlabel(r'BSA concentration ($\mu$g/ml)', size=20)
plt.ylim(0,0.3)
plt.plot([0, 547], [0.15, 0.15], 'k-', lw=2, color='green', linestyle='dashed')
plt.plot([547, 547], [0.15, 0], 'k-', lw=2, color='green', linestyle='dashed')
plt.xticks(fontsize=18, color='black')
plt.yticks(fontsize=18,color='black')
fig.savefig('Standard curve_BSA.png', dpi=500)
Figure S8 Dot blots carried out on increasing S. pombe JB35 protein masses, to determine the optimal primary and secondary antibody combination¶
In [10]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Dot blots.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
In [106]:
fig, ax1 = plt.subplots(figsize=(9,6))
x = [250, 416.67, 583.33, 833.33, 1166.67, 1380]
y = [4144.581, 6458.903, 8950.874, 10189.388, 8647.924, 14163.886]
ax1.set_ylabel(r'Signal intensity', size=20)
ax1.set_xlabel(r'Protein concentration ($\mu$g/ml)', size=20)
plt.ylim(0,15000)
plt.plot([417, 417], [14000, 0], 'k-', lw=2, color='red', linestyle='dashed')
plt.plot([583, 583], [14000, 0], 'k-', lw=2, color='red', linestyle='dashed')
plt.plot(x,y, color='black', marker = 'o')
plt.xticks(fontsize=18, color='black')
plt.xticks(rotation=30)
plt.yticks(fontsize=18,color='black')
fig.savefig('Dot blot quantification.png', dpi=500, bbox_inches='tight')
Figure S9 S. pombe JB35 sample protein standard curve¶
In [59]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Western blotting and cell viability analyses\Standard curve_2.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Western blotting and cell viability analyses\Standard curve_2.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Western blotting and cell viability analyses\Standard curve_2.csv')
Standard_curve_2 = pd.read_csv('Standard curve_2.csv')
Standard_curve_2["Corrected_OD_Rep1"] = (Standard_curve_2['Rep_1'] - Standard_curve_2['Blank'])
Standard_curve_2["Corrected_OD_Rep2"] = (Standard_curve_2['Rep_2'] - Standard_curve_2['Blank'])
Standard_curve_2["Corrected_OD_Rep3"] = (Standard_curve_2['Rep_3'] - Standard_curve_2['Blank'])
Standard_curve_2['Average_OD'] = Standard_curve_2.iloc[:, [5,6,7]].mean(axis=1)
Standard_curve_2['Std error of average OD'] = Standard_curve_2.iloc[:, [5,6,7]].sem(axis=1)
Standard_curve_2
Out[59]:
In [97]:
fig, ax1 = plt.subplots(figsize=(9,6))
x = Standard_curve_2['Protein concentration (ug/ml)']
y = Standard_curve_2['Average_OD']
yerror = Standard_curve_2['Std error of average OD']
xerror = Standard_curve_2['Std error of average OD']
#plt.plot(x,y)
plt.errorbar(x,y,yerr=yerror,xerr=xerror, color='black', capsize=3)
ax1.set_ylabel(r'Absorbance (562 nm)', size=20)
ax1.set_xlabel(r'Protein concentration ($\mu$g/ml)', size=20)
plt.ylim(0,0.3)
plt.xlim(200,600)
plt.plot([0, 400], [0.15, 0.15], 'k-', lw=2, color='green', linestyle='dashed')
plt.plot([400, 400], [0.15, 0], 'k-', lw=2, color='green', linestyle='dashed')
plt.xticks(fontsize=18, color='black')
plt.xticks(rotation=30)
plt.yticks(fontsize=18,color='black')
fig.savefig('Standard curve_protein.png', dpi=500)
Figure S10 Confirmation of dot blot analyses and sizing Trx1¶
In [14]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Linear_range_blot.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
In [99]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Western blotting and cell viability analyses\Sizing Trx1.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Western blotting and cell viability analyses\Sizing Trx1.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Western blotting and cell viability analyses\Sizing Trx1.csv')
Sizing_Trx1 = pd.read_csv('Sizing Trx1.csv')
In [101]:
fig, ax1 = plt.subplots(figsize=(9,6))
x = Sizing_Trx1['Distance migrated (mm)']
y = Sizing_Trx1['MWM (kDa)']
#plt.plot(x,y)
ax1.set_ylabel(r'MWM (kDa)', size=20)
ax1.set_xlabel(r'Distance migrated (mm)', size=20)
plt.ylim(0,300)
plt.xlim(0,100)
plt.plot(x,y, color='black', marker = 'o')
plt.plot([0, 84], [12, 12], 'k-', lw=2, color='blue', linestyle='dashed')
plt.plot([84, 84], [12, 0], 'k-', lw=2, color='blue', linestyle='dashed')
plt.xticks(fontsize=18, color='black')
plt.yticks(fontsize=18,color='black')
fig.savefig('Standard curve_sizing Trx.png', dpi=500)
Figure S11 Primary antibody specificity control using a non-tagged Trx1 protein extract¶
In [17]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Primary antibody specificity.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
Figure S12 Secondary antibody specificity control (no primary antibody), with a non-tagged Trx1 protein extract and purified, recombinant Trx1¶
In [19]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Antibody_control.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
Figure S13 Oxidized ( 2 mM diamide) and reduced ( 1 mM DTT) S. pombe JB35 Trx1 controls¶
In [20]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Ox and Red controls.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
Section S3 - MTT assay cell viability results and analyses¶
Cell viability stressor tests were carried out on Schizosaccharomyces pombe (JB35 cells, FLAG-tagged Trx1) using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) tetrazolium reduction assay. This assay works due to the reduction of a yellow tetrazolium salt by metabolically viable cells to purple formazan crystals, that can be spectrophotometrically quantified.
Stressors :
Control - No stress
Hydrogen peroxide (H2O2) - 100, 300, 500 & 1250 $\mu$M
Heat - 50$^\circ$C
Cadmium sulfate (CdSO4) - 8 mM
Potassium ferricynaide (K3Fe(CN)6) - 30 mM
3.1 No stress control¶
In [2]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Viability_No stress.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Viability_No stress.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Viability_No stress.csv')
Viability_no_stress = pd.read_csv('Viability_No stress.csv')
Viability_no_stress["Corrected_OD_Rep1"] = (Viability_no_stress['Rep_1'] - Viability_no_stress['Blank'])
Viability_no_stress["Corrected_OD_Rep2"] = (Viability_no_stress['Rep_2'] - Viability_no_stress['Blank'])
Viability_no_stress["Corrected_OD_Rep3"] = (Viability_no_stress['Rep_3'] - Viability_no_stress['Blank'])
Viability_no_stress['Average_OD'] = Viability_no_stress.iloc[:, [5,6,7]].mean(axis=1)
Viability_no_stress['Std error of average OD'] = Viability_no_stress.iloc[:, [5,6,7]].sem(axis=1)
Viability_no_stress
Out[2]:
In [22]:
Time0=([1.026,1.068,1.180])
Time60=([1.697,1.765,1.900])
from scipy import stats
print ('No stress control - Time (min) 0 vs 60')
f=stats.ttest_ind(Time0,Time60)
print (f)
print ('')
3.2 Hydrogen peroxide (100 $\mu$M)¶
In [3]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Viability 100uM H2O2.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Viability 100uM H2O2.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Viability 100uM H2O2.csv')
Viability_100uM_H2O2 = pd.read_csv('Viability 100uM H2O2.csv')
Viability_100uM_H2O2["Corrected_OD_Rep1"] = (Viability_100uM_H2O2['Rep_1'] - Viability_100uM_H2O2['Blank'])
Viability_100uM_H2O2["Corrected_OD_Rep2"] = (Viability_100uM_H2O2['Rep_2'] - Viability_100uM_H2O2['Blank'])
Viability_100uM_H2O2["Corrected_OD_Rep3"] = (Viability_100uM_H2O2['Rep_3'] - Viability_100uM_H2O2['Blank'])
Viability_100uM_H2O2['Average_OD'] = Viability_100uM_H2O2.iloc[:, [5,6,7]].mean(axis=1)
Viability_100uM_H2O2['Std error of average OD'] = Viability_100uM_H2O2.iloc[:, [5,6,7]].sem(axis=1)
Viability_100uM_H2O2
Out[3]:
In [24]:
Time0=([1.301,1.030,0.880])
Time60=([1.518,1.354,1.479])
from scipy import stats
print ('100uM H2O2 - Time (min) 0 vs 60')
f=stats.ttest_ind(Time0,Time60)
print (f)
print ('')
3.3 Hydrogen peroxide (300 $\mu$M)¶
In [4]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Viability 300uM H2O2.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Viability 300uM H2O2.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Viability 300uM H2O2.csv')
Viability_300uM_H2O2 = pd.read_csv('Viability 300uM H2O2.csv')
Viability_300uM_H2O2["Corrected_OD_Rep1"] = (Viability_300uM_H2O2['Rep_1'] - Viability_300uM_H2O2['Blank'])
Viability_300uM_H2O2["Corrected_OD_Rep2"] = (Viability_300uM_H2O2['Rep_2'] - Viability_300uM_H2O2['Blank'])
Viability_300uM_H2O2["Corrected_OD_Rep3"] = (Viability_300uM_H2O2['Rep_3'] - Viability_300uM_H2O2['Blank'])
Viability_300uM_H2O2['Average_OD'] = Viability_300uM_H2O2.iloc[:, [5,6,7]].mean(axis=1)
Viability_300uM_H2O2['Std error of average OD'] = Viability_300uM_H2O2.iloc[:, [5,6,7]].sem(axis=1)
Viability_300uM_H2O2
Out[4]:
In [26]:
Time0=([0.897,0.979,0.987])
Time60=([0.629,0.713,0.695])
from scipy import stats
print ('300uM H2O2 - Time (min) 0 vs 60')
f=stats.ttest_ind(Time0,Time60)
print (f)
print ('')
3.4 Hydrogen peroxide (500 $\mu$M)¶
In [5]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Viability 500uM H2O2.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Viability 500uM H2O2.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Viability 500uM H2O2.csv')
Viability_500uM_H2O2 = pd.read_csv('Viability 500uM H2O2.csv')
Viability_500uM_H2O2["Corrected_OD_Rep1"] = (Viability_500uM_H2O2['Rep_1'] - Viability_500uM_H2O2['Blank'])
Viability_500uM_H2O2["Corrected_OD_Rep2"] = (Viability_500uM_H2O2['Rep_2'] - Viability_500uM_H2O2['Blank'])
Viability_500uM_H2O2["Corrected_OD_Rep3"] = (Viability_500uM_H2O2['Rep_3'] - Viability_500uM_H2O2['Blank'])
Viability_500uM_H2O2['Average_OD'] = Viability_500uM_H2O2.iloc[:, [5,6,7]].mean(axis=1)
Viability_500uM_H2O2['Std error of average OD'] = Viability_500uM_H2O2.iloc[:, [5,6,7]].sem(axis=1)
Viability_500uM_H2O2
Out[5]:
In [28]:
Time0=([1.058,1.032,1.032])
Time60=([0.272,0.464,0.334])
from scipy import stats
print ('500uM H2O2 - Time (min) 0 vs 60')
f=stats.ttest_ind(Time0,Time60)
print (f)
print ('')
3.5 Hydrogen peroxide (1250 $\mu$M)¶
In [6]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Viability 1250uM H2O2.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Viability 1250uM H2O2.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Viability 1250uM H2O2.csv')
Viability_1250uM_H2O2 = pd.read_csv('Viability 1250uM H2O2.csv')
Viability_1250uM_H2O2["Corrected_OD_Rep1"] = (Viability_1250uM_H2O2['Rep_1'] - Viability_1250uM_H2O2['Blank'])
Viability_1250uM_H2O2["Corrected_OD_Rep2"] = (Viability_1250uM_H2O2['Rep_2'] - Viability_1250uM_H2O2['Blank'])
Viability_1250uM_H2O2["Corrected_OD_Rep3"] = (Viability_1250uM_H2O2['Rep_3'] - Viability_1250uM_H2O2['Blank'])
Viability_1250uM_H2O2['Average_OD'] = Viability_1250uM_H2O2.iloc[:, [5,6,7]].mean(axis=1)
Viability_1250uM_H2O2['Std error of average OD'] = Viability_1250uM_H2O2.iloc[:, [5,6,7]].sem(axis=1)
Viability_1250uM_H2O2
Out[6]:
In [30]:
Time0=([2.588,2.664,2.152])
Time60=([0.209,0.209,0.203])
from scipy import stats
print ('1250uM H2O2 - Time (min) 0 vs 60')
f=stats.ttest_ind(Time0,Time60)
print (f)
print ('')
3.6 Heat (50$^\circ$C)¶
In [7]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Viability Heat.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Viability Heat.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Viability Heat.csv')
Viability_Heat = pd.read_csv('Viability Heat.csv')
Viability_Heat["Corrected_OD_Rep1"] = (Viability_Heat['Rep_1'] - Viability_Heat['Blank'])
Viability_Heat["Corrected_OD_Rep2"] = (Viability_Heat['Rep_2'] - Viability_Heat['Blank'])
Viability_Heat["Corrected_OD_Rep3"] = (Viability_Heat['Rep_3'] - Viability_Heat['Blank'])
Viability_Heat['Average_OD'] = Viability_Heat.iloc[:, [5,6,7]].mean(axis=1)
Viability_Heat['Std error of average OD'] = Viability_Heat.iloc[:, [5,6,7]].sem(axis=1)
Viability_Heat
Out[7]:
In [32]:
Time0=([1.051,1.127,1.299])
Time60=([0.577,0.579,0.720])
from scipy import stats
print ('Heat - Time (min) 0 vs 60')
f=stats.ttest_ind(Time0,Time60)
print (f)
print ('')
3.7 Cadmium sulfate (8 mM)¶
In [8]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Viability Cadmium.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Viability Cadmium.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Viability Cadmium.csv')
Viability_Cadmium = pd.read_csv('Viability Cadmium.csv')
Viability_Cadmium["Corrected_OD_Rep1"] = (Viability_Cadmium['Rep_1'] - Viability_Cadmium['Blank'])
Viability_Cadmium["Corrected_OD_Rep2"] = (Viability_Cadmium['Rep_2'] - Viability_Cadmium['Blank'])
Viability_Cadmium["Corrected_OD_Rep3"] = (Viability_Cadmium['Rep_3'] - Viability_Cadmium['Blank'])
Viability_Cadmium['Average_OD'] = Viability_Cadmium.iloc[:, [5,6,7]].mean(axis=1)
Viability_Cadmium['Std error of average OD'] = Viability_Cadmium.iloc[:, [5,6,7]].sem(axis=1)
Viability_Cadmium
Out[8]:
In [34]:
Time0=([2.121,1.937,2.191])
Time60=([0.092,0.182,0.101])
from scipy import stats
print ('Cadmium - Time (min) 0 vs 60')
f=stats.ttest_ind(Time0,Time60)
print (f)
print ('')
3.8 Potassium ferricyanide (30 mM)¶
In [9]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Viability Cyanide.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Viability Cyanide.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Viability Cyanide.csv')
Viability_Cyanide = pd.read_csv('Viability Cyanide.csv')
Viability_Cyanide["Corrected_OD_Rep1"] = (Viability_Cyanide['Rep_1'] - Viability_Cyanide['Blank'])
Viability_Cyanide["Corrected_OD_Rep2"] = (Viability_Cyanide['Rep_2'] - Viability_Cyanide['Blank'])
Viability_Cyanide["Corrected_OD_Rep3"] = (Viability_Cyanide['Rep_3'] - Viability_Cyanide['Blank'])
Viability_Cyanide['Average_OD'] = Viability_Cyanide.iloc[:, [5,6,7]].mean(axis=1)
Viability_Cyanide['Std error of average OD'] = Viability_Cyanide.iloc[:, [5,6,7]].sem(axis=1)
Viability_Cyanide
Out[9]:
In [36]:
Time0=([2.299,2.142,2.301])
Time60=([1.102,1.264,1.283])
from scipy import stats
print ('Cyanide - Time (min) 0 vs 60')
f=stats.ttest_ind(Time0,Time60)
print (f)
print ('')
3.9 Cell viability plotted against time for all stressors¶
In [55]:
fig, ax1 = plt.subplots(figsize=(10,7))
x1 = Viability_no_stress['Time (min)']
y1 = Viability_no_stress['Average_OD']
y1error = Viability_no_stress['Std error of average OD']
x1error = Viability_no_stress['Std error of average OD']
plt.plot(x1,y1, color='royalblue', marker= 'o', label='No stress control', linewidth=4, markersize=10)
ax1.errorbar(x1,y1,yerr=y1error,xerr=x1error, color='royalblue', capsize=4, linewidth=2)
ax1.set_ylabel(r'Absorbance (562 nm)', size=20)
ax1.set_xlabel(r'Time (min)', size=20)
plt.legend(['No stress control'], frameon=False)
plt.legend(bbox_to_anchor=(1.35, 1), loc='upper right', borderaxespad=0, fontsize=18)
plt.ylim(0,3)
ax1.tick_params(axis='x', labelsize=18)
ax1.tick_params(axis='y', labelsize=18)
fig.savefig('Viability_No stress control.png', dpi=500, bbox_inches='tight')
In [16]:
fig, ax1 = plt.subplots(figsize=(10,7))
x2 = Viability_100uM_H2O2['Time (min)']
y2 = Viability_100uM_H2O2['Average_OD']
y2error = Viability_100uM_H2O2['Std error of average OD']
x2error = Viability_100uM_H2O2['Std error of average OD']
x3 = Viability_300uM_H2O2['Time (min)']
y3 = Viability_300uM_H2O2['Average_OD']
y3error = Viability_300uM_H2O2['Std error of average OD']
x3error = Viability_300uM_H2O2['Std error of average OD']
x4 = Viability_500uM_H2O2['Time (min)']
y4 = Viability_500uM_H2O2['Average_OD']
y4error = Viability_500uM_H2O2['Std error of average OD']
x4error = Viability_500uM_H2O2['Std error of average OD']
x5 = Viability_1250uM_H2O2['Time (min)']
y5 = Viability_1250uM_H2O2['Average_OD']
y5error = Viability_1250uM_H2O2['Std error of average OD']
x5error = Viability_1250uM_H2O2['Std error of average OD']
plt.plot(x2,y2, color='darkorange', marker= '>', label=r'100 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=4, markersize=10)
plt.plot(x3,y3, color='seagreen', marker= 's', label=r'300 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=4, markersize=10)
plt.plot(x4,y4, color='crimson', marker= 'p', label=r'500 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=4, markersize=10)
plt.plot(x5,y5, color='darkviolet', marker= '*', label=r'1250 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=4, markersize=10)
ax1.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='darkorange', capsize=4, linewidth=2)
ax1.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='seagreen', capsize=4, linewidth=2)
ax1.errorbar(x4,y4,yerr=y4error,xerr=x4error, color='crimson', capsize=4, linewidth=2)
ax1.errorbar(x5,y5,yerr=y5error,xerr=x5error, color='darkviolet', capsize=4, linewidth=2)
ax1.set_ylabel(r'Absorbance (562 nm)', size=20)
ax1.set_xlabel(r'Time (min)', size=20)
plt.legend(['100 $\mu$M $H_2O_2$','300 $\mu$M $H_2O_2$','500 $\mu$M $H_2O_2$','1250 $\mu$M $H_2O_2$'], frameon=False)
plt.legend(bbox_to_anchor=(1.32, 1), loc='upper right', borderaxespad=0, fontsize=18)
plt.ylim(0,3)
ax1.tick_params(axis='x', labelsize=18)
ax1.tick_params(axis='y', labelsize=18)
fig.savefig('Viability_H2O2 stressors.png', dpi=500, bbox_inches='tight')
In [18]:
fig, ax1 = plt.subplots(figsize=(10,7))
x2 = Viability_Heat['Time (min)']
y2 = Viability_Heat['Average_OD']
y2error = Viability_Heat['Std error of average OD']
x2error = Viability_Heat['Std error of average OD']
x3 = Viability_Cadmium['Time (min)']
y3 = Viability_Cadmium['Average_OD']
y3error = Viability_Cadmium['Std error of average OD']
x3error = Viability_Cadmium['Std error of average OD']
x4 = Viability_Cyanide['Time (min)']
y4 = Viability_Cyanide['Average_OD']
y4error = Viability_Cyanide['Std error of average OD']
x4error = Viability_Cyanide['Std error of average OD']
plt.plot(x2,y2, color='magenta', marker= 'H', label='50$^\circ$C Heat', linewidth=4, markersize=10)
plt.plot(x3,y3, color='limegreen', marker= 'D', label=r'8 mM ${\rm CdSO_4}$', linewidth=4, markersize=10)
plt.plot(x4,y4, color='deepskyblue', marker= 'X', label=r'30 mM ${\rm K_3Fe(CN)_6}$', linewidth=4, markersize=10)
ax1.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='magenta', capsize=4, linewidth=2)
ax1.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='limegreen', capsize=4, linewidth=2)
ax1.errorbar(x4,y4,yerr=y4error,xerr=x4error, color='deepskyblue', capsize=4, linewidth=2)
ax1.set_ylabel(r'Absorbance (562 nm)', size=20)
ax1.set_xlabel(r'Time (min)', size=20)
plt.legend(['50$^\circ$C Heat','8 mM $CdSO_4$','30 mM $K_3Fe(CN)_6$'], frameon=False)
plt.legend(bbox_to_anchor=(1.38, 1), loc='upper right', borderaxespad=0, fontsize=18)
plt.ylim(0,3)
ax1.tick_params(axis='x', labelsize=18)
ax1.tick_params(axis='y', labelsize=18)
fig.savefig('Viability_Other stressors.png', dpi=500, bbox_inches='tight')
Section S4 - Stressor blots and thioredoxin redox charge analyses¶
Stressor time-course experiments were carried out on Schizosaccharomyces pombe (JB35 cells, FLAG-tagged Trx1) and redox western blotting analysis was used to analyze the samples.
Stressors :
Control - No stress
Hydrogen peroxide (H2O2) - 100, 300, 500 & 1250 $\mu$M
Heat - 50$^\circ$C
Cadmium sulfate (CdSO4) - 8 mM
Potassium ferricynaide (K3Fe(CN)6) - 30 mM
In [42]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('No stress_1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('No stress_2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('No stress_3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
No stress control - Analysis of oxidized and reduced bands¶
In [19]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\No stress control.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\No stress control.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\No stress control.csv')
No_stress = pd.read_csv('No stress control.csv')
No_stress['R1_Red_Total'] = No_stress.iloc[:, [1,2]].sum(axis=1)
col_name1="R1_Red_Total"
first_col = No_stress.pop(col_name1)
No_stress.insert(3, col_name1, first_col)
No_stress['R2_Red_Total'] = No_stress.iloc[:, [5,6]].sum(axis=1)
col_name2="R2_Red_Total"
second_col = No_stress.pop(col_name2)
No_stress.insert(7, col_name2, second_col)
No_stress['R3_Red_Total'] = No_stress.iloc[:, [9,10]].sum(axis=1)
col_name3="R3_Red_Total"
third_col = No_stress.pop(col_name3)
No_stress.insert(11, col_name3, third_col)
No_stress["R1_Red_Total/(R1_Total Trx)"] = No_stress['R1_Red_Total'].div((No_stress['R1_Red_Total'])+(No_stress['R1_Ox']))
No_stress["R2_Red_Total/(R2_Total Trx)"] = No_stress['R2_Red_Total'].div((No_stress['R2_Red_Total'])+(No_stress['R2_Ox']))
No_stress["R3_Red_Total/(R3_Total Trx)"] = No_stress['R3_Red_Total'].div((No_stress['R3_Red_Total'])+(No_stress['R3_Ox']))
No_stress['Average_Red_Total/Total_Trx'] = No_stress.iloc[:, [13,14,15]].mean(axis=1)
No_stress['Std error for Trx redox charge'] = No_stress.iloc[:, [13,14,15]].sem(axis=1)
No_stress
Out[19]:
In [44]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('100uM_H2O2_1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('100uM_H2O2_2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('100uM_H2O2_3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
100 𝜇M Hydrogen peroxide - Analysis of oxidized and reduced bands¶
In [20]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\100 uM H2O2.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\100 uM H2O2.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\100 uM H2O2.csv')
H2O2_100uM = pd.read_csv('100 uM H2O2.csv')
H2O2_100uM['R1_Red_Total'] = H2O2_100uM.iloc[:, [1,2]].sum(axis=1)
col_name1="R1_Red_Total"
first_col = H2O2_100uM.pop(col_name1)
H2O2_100uM.insert(3, col_name1, first_col)
H2O2_100uM['R2_Red_Total'] = H2O2_100uM.iloc[:, [5,6]].sum(axis=1)
col_name2="R2_Red_Total"
second_col = H2O2_100uM.pop(col_name2)
H2O2_100uM.insert(7, col_name2, second_col)
H2O2_100uM['R3_Red_Total'] = H2O2_100uM.iloc[:, [9,10]].sum(axis=1)
col_name3="R3_Red_Total"
third_col = H2O2_100uM.pop(col_name3)
H2O2_100uM.insert(11, col_name3, third_col)
H2O2_100uM["R1_Red_Total/(R1_Total Trx)"] = H2O2_100uM['R1_Red_Total'].div((H2O2_100uM['R1_Red_Total'])+(H2O2_100uM['R1_Ox']))
H2O2_100uM["R2_Red_Total/(R2_Total Trx)"] = H2O2_100uM['R2_Red_Total'].div((H2O2_100uM['R2_Red_Total'])+(H2O2_100uM['R2_Ox']))
H2O2_100uM["R3_Red_Total/(R3_Total Trx)"] = H2O2_100uM['R3_Red_Total'].div((H2O2_100uM['R3_Red_Total'])+(H2O2_100uM['R3_Ox']))
H2O2_100uM['Average_Red_Total/Total_Trx'] = H2O2_100uM.iloc[:, [13,14,15]].mean(axis=1)
H2O2_100uM['Std error for Trx redox charge'] = H2O2_100uM.iloc[:, [13,14,15]].sem(axis=1)
H2O2_100uM
Out[20]:
In [46]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('300uM_H2O2_1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('300uM_H2O2_2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('300uM_H2O2_3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
300 𝜇M Hydrogen peroxide - Analysis of oxidized and reduced bands¶
In [21]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\300 uM H2O2.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\300 uM H2O2.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\300 uM H2O2.csv')
H2O2_300uM = pd.read_csv('300 uM H2O2.csv')
H2O2_300uM['R1_Red_Total'] = H2O2_300uM.iloc[:, [1,2]].sum(axis=1)
col_name1="R1_Red_Total"
first_col = H2O2_300uM.pop(col_name1)
H2O2_300uM.insert(3, col_name1, first_col)
H2O2_300uM['R2_Red_Total'] = H2O2_300uM.iloc[:, [5,6]].sum(axis=1)
col_name2="R2_Red_Total"
second_col = H2O2_300uM.pop(col_name2)
H2O2_300uM.insert(7, col_name2, second_col)
H2O2_300uM['R3_Red_Total'] = H2O2_300uM.iloc[:, [9,10]].sum(axis=1)
col_name3="R3_Red_Total"
third_col = H2O2_300uM.pop(col_name3)
H2O2_300uM.insert(11, col_name3, third_col)
H2O2_300uM["R1_Red_Total/(R1_Total Trx)"] = H2O2_300uM['R1_Red_Total'].div((H2O2_300uM['R1_Red_Total'])+(H2O2_300uM['R1_Ox']))
H2O2_300uM["R2_Red_Total/(R2_Total Trx)"] = H2O2_300uM['R2_Red_Total'].div((H2O2_300uM['R2_Red_Total'])+(H2O2_300uM['R2_Ox']))
H2O2_300uM["R3_Red_Total/(R3_Total Trx)"] = H2O2_300uM['R3_Red_Total'].div((H2O2_300uM['R3_Red_Total'])+(H2O2_300uM['R3_Ox']))
H2O2_300uM['Average_Red_Total/Total_Trx'] = H2O2_300uM.iloc[:, [13,14,15]].mean(axis=1)
H2O2_300uM['Std error for Trx redox charge'] = H2O2_300uM.iloc[:, [13,14,15]].sem(axis=1)
H2O2_300uM
Out[21]:
In [48]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('500uM_H2O2_1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('500uM_H2O2_2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('500uM_H2O2_3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
500 𝜇M Hydrogen peroxide - Analysis of oxidized and reduced bands¶
In [22]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\500 uM H2O2.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\500 uM H2O2.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\500 uM H2O2.csv')
H2O2_500uM = pd.read_csv('500 uM H2O2.csv')
H2O2_500uM['R1_Red_Total'] = H2O2_500uM.iloc[:, [1,2]].sum(axis=1)
col_name1="R1_Red_Total"
first_col = H2O2_500uM.pop(col_name1)
H2O2_500uM.insert(3, col_name1, first_col)
H2O2_500uM['R2_Red_Total'] = H2O2_500uM.iloc[:, [5,6]].sum(axis=1)
col_name2="R2_Red_Total"
second_col = H2O2_500uM.pop(col_name2)
H2O2_500uM.insert(7, col_name2, second_col)
H2O2_500uM['R3_Red_Total'] = H2O2_500uM.iloc[:, [9,10]].sum(axis=1)
col_name3="R3_Red_Total"
third_col = H2O2_500uM.pop(col_name3)
H2O2_500uM.insert(11, col_name3, third_col)
H2O2_500uM["R1_Red_Total/(R1_Total Trx)"] = H2O2_500uM['R1_Red_Total'].div((H2O2_500uM['R1_Red_Total'])+(H2O2_500uM['R1_Ox']))
H2O2_500uM["R2_Red_Total/(R2_Total Trx)"] = H2O2_500uM['R2_Red_Total'].div((H2O2_500uM['R2_Red_Total'])+(H2O2_500uM['R2_Ox']))
H2O2_500uM["R3_Red_Total/(R3_Total Trx)"] = H2O2_500uM['R3_Red_Total'].div((H2O2_500uM['R3_Red_Total'])+(H2O2_500uM['R3_Ox']))
H2O2_500uM['Average_Red_Total/Total_Trx'] = H2O2_500uM.iloc[:, [13,14,15]].mean(axis=1)
H2O2_500uM['Std error for Trx redox charge'] = H2O2_500uM.iloc[:, [13,14,15]].sem(axis=1)
H2O2_500uM
Out[22]:
In [50]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('1250uM_H2O2_1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('1250uM_H2O2_2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('1250uM_H2O2_3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
1250 𝜇M Hydrogen peroxide - Analysis of oxidized and reduced bands¶
In [23]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\1250 uM H2O2.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\1250 uM H2O2.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\1250 uM H2O2.csv')
H2O2_1250uM = pd.read_csv('1250 uM H2O2.csv')
H2O2_1250uM['R1_Red_Total'] = H2O2_1250uM.iloc[:, [1,2]].sum(axis=1)
col_name1="R1_Red_Total"
first_col = H2O2_1250uM.pop(col_name1)
H2O2_1250uM.insert(3, col_name1, first_col)
H2O2_1250uM['R2_Red_Total'] = H2O2_1250uM.iloc[:, [5,6]].sum(axis=1)
col_name2="R2_Red_Total"
second_col = H2O2_1250uM.pop(col_name2)
H2O2_1250uM.insert(7, col_name2, second_col)
H2O2_1250uM['R3_Red_Total'] = H2O2_1250uM.iloc[:, [9,10]].sum(axis=1)
col_name3="R3_Red_Total"
third_col = H2O2_1250uM.pop(col_name3)
H2O2_1250uM.insert(11, col_name3, third_col)
H2O2_1250uM["R1_Red_Total/(R1_Total Trx)"] = H2O2_1250uM['R1_Red_Total'].div((H2O2_1250uM['R1_Red_Total'])+(H2O2_1250uM['R1_Ox']))
H2O2_1250uM["R2_Red_Total/(R2_Total Trx)"] = H2O2_1250uM['R2_Red_Total'].div((H2O2_1250uM['R2_Red_Total'])+(H2O2_1250uM['R2_Ox']))
H2O2_1250uM["R3_Red_Total/(R3_Total Trx)"] = H2O2_1250uM['R3_Red_Total'].div((H2O2_1250uM['R3_Red_Total'])+(H2O2_1250uM['R3_Ox']))
H2O2_1250uM['Average_Red_Total/Total_Trx'] = H2O2_1250uM.iloc[:, [13,14,15]].mean(axis=1)
H2O2_1250uM['Std error for Trx redox charge'] = H2O2_1250uM.iloc[:, [13,14,15]].sem(axis=1)
H2O2_1250uM
Out[23]:
In [52]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Heat_1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Heat_2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Heat_3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
50$^\circ$C Heat - Analysis of oxidized and reduced bands¶
In [24]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Heat.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Heat.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Heat.csv')
Heat = pd.read_csv('Heat.csv')
Heat['R1_Red_Total'] = Heat.iloc[:, [1,2]].sum(axis=1)
col_name1="R1_Red_Total"
first_col = Heat.pop(col_name1)
Heat.insert(3, col_name1, first_col)
Heat['R2_Red_Total'] = Heat.iloc[:, [5,6]].sum(axis=1)
col_name2="R2_Red_Total"
second_col = Heat.pop(col_name2)
Heat.insert(7, col_name2, second_col)
Heat['R3_Red_Total'] = Heat.iloc[:, [9,10]].sum(axis=1)
col_name3="R3_Red_Total"
third_col = Heat.pop(col_name3)
Heat.insert(11, col_name3, third_col)
Heat["R1_Red_Total/(R1_Total Trx)"] = Heat['R1_Red_Total'].div((Heat['R1_Red_Total'])+(Heat['R1_Ox']))
Heat["R2_Red_Total/(R2_Total Trx)"] = Heat['R2_Red_Total'].div((Heat['R2_Red_Total'])+(Heat['R2_Ox']))
Heat["R3_Red_Total/(R3_Total Trx)"] = Heat['R3_Red_Total'].div((Heat['R3_Red_Total'])+(Heat['R3_Ox']))
Heat['Average_Red_Total/Total_Trx'] = Heat.iloc[:, [13,14,15]].mean(axis=1)
Heat['Std error for Trx redox charge'] = Heat.iloc[:, [13,14,15]].sem(axis=1)
Heat
Out[24]:
In [54]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cadmium_1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cadmium_2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cadmium_3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
8 mM Cadmium sulfate - Analysis of oxidized and reduced bands¶
In [25]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Cadmium.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Cadmium.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Cadmium.csv')
Cadmium = pd.read_csv('Cadmium.csv')
Cadmium['R1_Red_Total'] = Cadmium.iloc[:, [1,2]].sum(axis=1)
col_name1="R1_Red_Total"
first_col = Cadmium.pop(col_name1)
Cadmium.insert(3, col_name1, first_col)
Cadmium['R2_Red_Total'] = Cadmium.iloc[:, [5,6]].sum(axis=1)
col_name2="R2_Red_Total"
second_col = Cadmium.pop(col_name2)
Cadmium.insert(7, col_name2, second_col)
Cadmium['R3_Red_Total'] = Cadmium.iloc[:, [9,10]].sum(axis=1)
col_name3="R3_Red_Total"
third_col = Cadmium.pop(col_name3)
Cadmium.insert(11, col_name3, third_col)
Cadmium["R1_Red_Total/(R1_Total Trx)"] = Cadmium['R1_Red_Total'].div((Cadmium['R1_Red_Total'])+(Cadmium['R1_Ox']))
Cadmium["R2_Red_Total/(R2_Total Trx)"] = Cadmium['R2_Red_Total'].div((Cadmium['R2_Red_Total'])+(Cadmium['R2_Ox']))
Cadmium["R3_Red_Total/(R3_Total Trx)"] = Cadmium['R3_Red_Total'].div((Cadmium['R3_Red_Total'])+(Cadmium['R3_Ox']))
Cadmium['Average_Red_Total/Total_Trx'] = Cadmium.iloc[:, [13,14,15]].mean(axis=1)
Cadmium['Std error for Trx redox charge'] = Cadmium.iloc[:, [13,14,15]].sem(axis=1)
Cadmium
Out[25]:
In [56]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cyanide_1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cyanide_2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cyanide_3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
30 mM Potassium ferricyanide - Analysis of oxidized and reduced bands¶
In [26]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Cyanide.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Cyanide.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Cyanide.csv')
Cyanide = pd.read_csv('Cyanide.csv')
Cyanide['R1_Red_Total'] = Cyanide.iloc[:, [1,2]].sum(axis=1)
col_name1="R1_Red_Total"
first_col = Cyanide.pop(col_name1)
Cyanide.insert(3, col_name1, first_col)
Cyanide['R2_Red_Total'] =Cyanide.iloc[:, [5,6]].sum(axis=1)
col_name2="R2_Red_Total"
second_col = Cyanide.pop(col_name2)
Cyanide.insert(7, col_name2, second_col)
Cyanide['R3_Red_Total'] = Cyanide.iloc[:, [9,10]].sum(axis=1)
col_name3="R3_Red_Total"
third_col = Cyanide.pop(col_name3)
Cyanide.insert(11, col_name3, third_col)
Cyanide["R1_Red_Total/(R1_Total Trx)"] = Cyanide['R1_Red_Total'].div((Cyanide['R1_Red_Total'])+(Cyanide['R1_Ox']))
Cyanide["R2_Red_Total/(R2_Total Trx)"] = Cyanide['R2_Red_Total'].div((Cyanide['R2_Red_Total'])+(Cyanide['R2_Ox']))
Cyanide["R3_Red_Total/(R3_Total Trx)"] = Cyanide['R3_Red_Total'].div((Cyanide['R3_Red_Total'])+(Cyanide['R3_Ox']))
Cyanide['Average_Red_Total/Total_Trx'] = Cyanide.iloc[:, [13,14,15]].mean(axis=1)
Cyanide['Std error for Trx redox charge'] = Cyanide.iloc[:, [13,14,15]].sem(axis=1)
Cyanide
Out[26]:
4.9 Thioredoxin redox charge plotted against time for all stressors¶
In [52]:
fig, ax1 = plt.subplots(figsize=(10,7))
x1 = No_stress['Time (min)']
y1 = No_stress['Average_Red_Total/Total_Trx']
y1error = No_stress['Std error for Trx redox charge']
x1error = No_stress['Std error for Trx redox charge']
plt.plot(x1,y1, color='royalblue', marker= 'o', label='No stress control', linewidth=4, markersize=10)
ax1.errorbar(x1,y1,yerr=y1error,xerr=x1error, color='royalblue', capsize=4, linewidth=2)
ax1.set_ylabel(r'Trx redox charge', size=20)
ax1.set_xlabel(r'Time (min)', size=20)
plt.legend(['No stress control'], frameon=False)
plt.legend(bbox_to_anchor=(1.36, 1), loc='upper right', borderaxespad=0, fontsize=18)
plt.ylim(0.3,1)
ax1.tick_params(axis='x', labelsize=18)
ax1.tick_params(axis='y', labelsize=18)
fig.savefig('Trx redox charge_No stress control.png', dpi=500, bbox_inches='tight')
In [51]:
fig, ax1 = plt.subplots(figsize=(10,7))
ax2=ax1.twinx()
ax1 = No_stress.plot('Time (min)','Average_Red_Total/Total_Trx', color = 'royalblue', ax=ax1, figsize=(9,6), marker = 'o', linewidth=4, markersize=10)
ax1.set_ylabel('Trx redox charge', color = 'royalblue', fontsize=20)
ax1.set_ylim(0.5,1)
ax1.tick_params(axis = 'y', colors = 'royalblue', labelsize=18)
ax2.spines['left'].set_color('royalblue')
x1 = No_stress['Time (min)']
y1 = No_stress['Average_Red_Total/Total_Trx']
y1error = No_stress['Std error for Trx redox charge']
x1error = No_stress['Std error for Trx redox charge']
ax1.errorbar(x1,y1,yerr=y1error,xerr=x1error, color='royalblue', capsize=4, linewidth=2)
ax2 = Viability_no_stress.plot('Time (min)','Average_OD', color = 'green', ax=ax2, marker = 'o', linewidth=4, markersize=10)
ax2.set_ylabel('Absorbance (562 nm)', color = 'green', fontsize=20)
ax2.set_ylim(1,2)
ax2.tick_params(axis = 'y', colors = 'green', labelsize=18)
ax2.spines['right'].set_color('green')
x2 = Viability_no_stress['Time (min)']
y2 = Viability_no_stress['Average_OD']
y2error = Viability_no_stress['Std error of average OD']
x2error = Viability_no_stress['Std error of average OD']
ax2.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='green', capsize=4, linewidth=2)
ax1.set_xlabel('Time (min)', color = 'Black', fontsize=20)
ax1.tick_params(axis='x', labelsize=18)
ax1.get_legend().remove()
ax2.get_legend().remove()
plt.plot()
fig.savefig('Trx redox charge & Viability double y axis_No stress control.png', dpi=500, bbox_inches='tight')
In [27]:
fig, ax1 = plt.subplots(figsize=(10,7))
x2 = H2O2_100uM['Time (min)']
y2 = H2O2_100uM['Average_Red_Total/Total_Trx']
y2error = H2O2_100uM['Std error for Trx redox charge']
x2error = H2O2_100uM['Std error for Trx redox charge']
x3 = H2O2_300uM['Time (min)']
y3 = H2O2_300uM['Average_Red_Total/Total_Trx']
y3error = H2O2_300uM['Std error for Trx redox charge']
x3error = H2O2_300uM['Std error for Trx redox charge']
x4 = H2O2_500uM['Time (min)']
y4 = H2O2_500uM['Average_Red_Total/Total_Trx']
y4error = H2O2_500uM['Std error for Trx redox charge']
x4error = H2O2_500uM['Std error for Trx redox charge']
x5 = H2O2_1250uM['Time (min)']
y5 = H2O2_1250uM['Average_Red_Total/Total_Trx']
y5error = H2O2_1250uM['Std error for Trx redox charge']
x5error = H2O2_1250uM['Std error for Trx redox charge']
plt.plot(x2,y2, color='darkorange', marker= '>', label=r'100 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=4, markersize=10)
plt.plot(x3,y3, color='seagreen', marker= 's', label=r'300 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=4, markersize=10)
plt.plot(x4,y4, color='crimson', marker= 'p', label=r'500 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=4, markersize=10)
plt.plot(x5,y5, color='darkviolet', marker= '*', label=r'1250 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=4, markersize=10)
ax1.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='darkorange', capsize=4, linewidth=2)
ax1.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='seagreen', capsize=4, linewidth=2)
ax1.errorbar(x4,y4,yerr=y4error,xerr=x4error, color='crimson', capsize=4, linewidth=2)
ax1.errorbar(x5,y5,yerr=y5error,xerr=x5error, color='darkviolet', capsize=4, linewidth=2)
ax1.set_ylabel(r'Trx redox charge', size=20)
ax1.set_xlabel(r'Time (min)', size=20)
plt.legend(['100 $\mu$M $H_2O_2$','300 $\mu$M $H_2O_2$','500 $\mu$M $H_2O_2$','1250 $\mu$M $H_2O_2$'], frameon=False)
plt.legend(bbox_to_anchor=(1.33, 1), loc='upper right', borderaxespad=0, fontsize=18)
plt.ylim(0.3,1)
ax1.tick_params(axis='x', labelsize=18)
ax1.tick_params(axis='y', labelsize=18)
fig.savefig('Trx redox charge_H2O2 stressors.png', dpi=500, bbox_inches='tight')
In [28]:
fig, ax1 = plt.subplots(figsize=(10,7))
x2 = Heat['Time (min)']
y2 = Heat['Average_Red_Total/Total_Trx']
y2error = Heat['Std error for Trx redox charge']
x2error = Heat['Std error for Trx redox charge']
x3 = Cadmium['Time (min)']
y3 = Cadmium['Average_Red_Total/Total_Trx']
y3error = Cadmium['Std error for Trx redox charge']
x3error = Cadmium['Std error for Trx redox charge']
x4 = Cyanide['Time (min)']
y4 = Cyanide['Average_Red_Total/Total_Trx']
y4error = Cyanide['Std error for Trx redox charge']
x4error = Cyanide['Std error for Trx redox charge']
plt.plot(x2,y2, color='magenta', marker= 'H', label='50$^\circ$C Heat', linewidth=4, markersize=10)
plt.plot(x3,y3, color='limegreen', marker= 'D', label=r'8 mM ${\rm CdSO_4}$', linewidth=4, markersize=10)
plt.plot(x4,y4, color='deepskyblue', marker= 'X', label=r'30 mM ${\rm K_3Fe(CN)_6}$', linewidth=4, markersize=10)
ax1.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='magenta', capsize=4, linewidth=2)
ax1.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='limegreen', capsize=4, linewidth=2)
ax1.errorbar(x4,y4,yerr=y4error,xerr=x4error, color='deepskyblue', capsize=4, linewidth=2)
ax1.set_ylabel(r'Trx redox charge', size=20)
ax1.set_xlabel(r'Time (min)', size=20)
plt.legend(['50$^\circ$C Heat','8 mM $CdSO_4$','30 mM $K_3Fe(CN)_6$'], frameon=False)
plt.legend(bbox_to_anchor=(1.38, 1), loc='upper right', borderaxespad=0, fontsize=18)
plt.ylim(0.3,1)
ax1.tick_params(axis='x', labelsize=18)
ax1.tick_params(axis='y', labelsize=18)
fig.savefig('Trx redox charge_Other stressors.png', dpi=500, bbox_inches='tight')
Section S5 - Thioredoxin redox charge and cell viability correlation plots¶
In [18]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
from scipy.spatial import distance
plt.figure(figsize=(10,7))
x = No_stress['Average_Red_Total/Total_Trx']
y = Viability_no_stress['Average_OD']
print('No stress control')
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
print('Distance correlation, P-value')
dcor, pval = distance_corr(x, y, seed=None)
print(round(dcor, 3), pval)
xerr = No_stress['Std error for Trx redox charge']
yerr = Viability_no_stress['Std error of average OD']
plt.errorbar(x, y, xerr = No_stress['Std error for Trx redox charge'], yerr = Viability_no_stress['Std error of average OD'], fmt='o', color="royalblue", capsize = 4, label='No stress control', linewidth=2)
plt.scatter(x, y, s=100, color = 'royalblue')
plt.xlabel(r'Trx redox charge',size=20, color='black')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='black')
plt.legend(['No stress control'], frameon=False)
plt.legend(fontsize=18)
plt.xlim([0.3, 1])
plt.ylim([0, 3])
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Correlation_No stress.png', dpi=500, bbox_inches='tight')
plt.show()
In [29]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
from scipy.spatial import distance
plt.figure(figsize=(10,7))
print('No stress control')
corr, pvalue = scipy.stats.pearsonr(No_stress['Average_Red_Total/Total_Trx'], Viability_no_stress['Average_OD'])
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(No_stress['Average_Red_Total/Total_Trx'], Viability_no_stress['Average_OD'])
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(No_stress['Average_Red_Total/Total_Trx'], Viability_no_stress['Average_OD'])
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(No_stress['Average_Red_Total/Total_Trx'], Viability_no_stress['Average_OD'])
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
print('Distance correlation, P-value')
dcor, pval = distance_corr(No_stress['Average_Red_Total/Total_Trx'], Viability_no_stress['Average_OD'], seed=None)
print(round(dcor, 3), pval)
print('100 uM Hydrogen peroxide')
corr, pvalue = scipy.stats.pearsonr(H2O2_100uM['Average_Red_Total/Total_Trx'] , Viability_100uM_H2O2['Average_OD'])
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(H2O2_100uM['Average_Red_Total/Total_Trx'] , Viability_100uM_H2O2['Average_OD'])
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(H2O2_100uM['Average_Red_Total/Total_Trx'] , Viability_100uM_H2O2['Average_OD'])
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(H2O2_100uM['Average_Red_Total/Total_Trx'] , Viability_100uM_H2O2['Average_OD'])
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
print('Distance correlation, P-value')
dcor, pval = distance_corr(H2O2_100uM['Average_Red_Total/Total_Trx'], Viability_100uM_H2O2['Average_OD'], seed=None)
print(round(dcor, 3), pval)
print('300 uM Hydrogen peroxide')
corr, pvalue = scipy.stats.pearsonr(H2O2_300uM['Average_Red_Total/Total_Trx'] , Viability_300uM_H2O2['Average_OD'])
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(H2O2_300uM['Average_Red_Total/Total_Trx'] , Viability_300uM_H2O2['Average_OD'])
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(H2O2_300uM['Average_Red_Total/Total_Trx'] , Viability_300uM_H2O2['Average_OD'])
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(H2O2_300uM['Average_Red_Total/Total_Trx'] , Viability_300uM_H2O2['Average_OD'])
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
print('Distance correlation, P-value')
dcor, pval = distance_corr(H2O2_300uM['Average_Red_Total/Total_Trx'], Viability_300uM_H2O2['Average_OD'], seed=None)
print(round(dcor, 3), pval)
print('500 uM Hydrogen peroxide')
corr, pvalue = scipy.stats.pearsonr(H2O2_500uM['Average_Red_Total/Total_Trx'] , Viability_500uM_H2O2['Average_OD'])
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(H2O2_500uM['Average_Red_Total/Total_Trx'] , Viability_500uM_H2O2['Average_OD'])
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(H2O2_500uM['Average_Red_Total/Total_Trx'] , Viability_500uM_H2O2['Average_OD'])
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(H2O2_500uM['Average_Red_Total/Total_Trx'] , Viability_500uM_H2O2['Average_OD'])
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
print('Distance correlation, P-value')
dcor, pval = distance_corr(H2O2_500uM['Average_Red_Total/Total_Trx'], Viability_500uM_H2O2['Average_OD'], seed=None)
print(round(dcor, 3), pval)
print('1250 uM Hydrogen peroxide')
corr, pvalue = scipy.stats.pearsonr(H2O2_1250uM['Average_Red_Total/Total_Trx'] , Viability_1250uM_H2O2['Average_OD'])
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(H2O2_1250uM['Average_Red_Total/Total_Trx'] , Viability_1250uM_H2O2['Average_OD'])
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(H2O2_1250uM['Average_Red_Total/Total_Trx'] , Viability_1250uM_H2O2['Average_OD'])
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(H2O2_1250uM['Average_Red_Total/Total_Trx'] , Viability_1250uM_H2O2['Average_OD'])
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
print('Distance correlation, P-value')
dcor, pval = distance_corr(H2O2_1250uM['Average_Red_Total/Total_Trx'], Viability_1250uM_H2O2['Average_OD'], seed=None)
print(round(dcor, 3), pval)
plt.errorbar(No_stress['Average_Red_Total/Total_Trx'] , Viability_no_stress['Average_OD'], xerr = No_stress['Std error for Trx redox charge'], yerr = Viability_no_stress['Std error of average OD'], fmt='o', color='royalblue', capsize = 4, label='No stress control', linewidth=2)
plt.errorbar(H2O2_100uM['Average_Red_Total/Total_Trx'] , Viability_100uM_H2O2['Average_OD'], xerr = H2O2_100uM['Std error for Trx redox charge'], yerr = Viability_100uM_H2O2['Std error of average OD'], fmt='o', color='darkorange', capsize = 4, label=r'100 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=2)
plt.errorbar(H2O2_300uM['Average_Red_Total/Total_Trx'] , Viability_300uM_H2O2['Average_OD'], xerr = H2O2_300uM['Std error for Trx redox charge'], yerr = Viability_300uM_H2O2['Std error of average OD'], fmt='o', color='seagreen', capsize = 4, label=r'300 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=2)
plt.errorbar(H2O2_500uM['Average_Red_Total/Total_Trx'] , Viability_500uM_H2O2['Average_OD'], xerr = H2O2_500uM['Std error for Trx redox charge'], yerr = Viability_500uM_H2O2['Std error of average OD'], fmt='o', color='crimson', capsize = 4, label=r'500 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=2)
plt.errorbar(H2O2_1250uM['Average_Red_Total/Total_Trx'] , Viability_1250uM_H2O2['Average_OD'], xerr = H2O2_1250uM['Std error for Trx redox charge'], yerr = Viability_1250uM_H2O2['Std error of average OD'], fmt='o', color='darkviolet', capsize = 4, label=r'1250 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=2)
plt.scatter(No_stress['Average_Red_Total/Total_Trx'] , Viability_no_stress['Average_OD'], s=100, color = 'royalblue')
plt.scatter(H2O2_100uM['Average_Red_Total/Total_Trx'] , Viability_100uM_H2O2['Average_OD'], s=100, color = 'darkorange')
plt.scatter(H2O2_300uM['Average_Red_Total/Total_Trx'] , Viability_300uM_H2O2['Average_OD'], s=100, color = 'seagreen')
plt.scatter(H2O2_500uM['Average_Red_Total/Total_Trx'] , Viability_500uM_H2O2['Average_OD'], s=100, color = 'crimson')
plt.scatter(H2O2_1250uM['Average_Red_Total/Total_Trx'] , Viability_1250uM_H2O2['Average_OD'], s=100, color = 'darkviolet')
plt.xlabel(r'Trx redox charge',size=20, color='black')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='black')
plt.legend(loc='best', frameon=False)
plt.legend(fontsize=18)
plt.xlim([0.3, 1])
plt.ylim([0, 3])
plt.xticks(fontsize=18, color='black')
plt.yticks(fontsize=18,color='black')
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Correlation_H2O2_stressors.png', dpi=500, bbox_inches='tight')
plt.show()
In [30]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
from scipy.spatial import distance
plt.figure(figsize=(10,7))
print('No stress control')
corr, pvalue = scipy.stats.pearsonr(No_stress['Average_Red_Total/Total_Trx'], Viability_no_stress['Average_OD'])
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(No_stress['Average_Red_Total/Total_Trx'], Viability_no_stress['Average_OD'])
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(No_stress['Average_Red_Total/Total_Trx'], Viability_no_stress['Average_OD'])
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(No_stress['Average_Red_Total/Total_Trx'], Viability_no_stress['Average_OD'])
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
print('Distance correlation, P-value')
dcor, pval = distance_corr(No_stress['Average_Red_Total/Total_Trx'], Viability_no_stress['Average_OD'], seed=None)
print(round(dcor, 3), pval)
print('Heat')
corr, pvalue = scipy.stats.pearsonr(Heat['Average_Red_Total/Total_Trx'] ,Viability_Heat['Average_OD'])
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(Heat['Average_Red_Total/Total_Trx'] , Viability_Heat['Average_OD'])
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(Heat['Average_Red_Total/Total_Trx'] , Viability_Heat['Average_OD'])
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(Heat['Average_Red_Total/Total_Trx'] , Viability_Heat['Average_OD'])
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
print('Distance correlation, P-value')
dcor, pval = distance_corr(Heat['Average_Red_Total/Total_Trx'] , Viability_Heat['Average_OD'], seed=None)
print(round(dcor, 3), pval)
print('Cadmium sulfate')
corr, pvalue = scipy.stats.pearsonr(Cadmium['Average_Red_Total/Total_Trx'] , Viability_Cadmium['Average_OD'])
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(Cadmium['Average_Red_Total/Total_Trx'] , Viability_Cadmium['Average_OD'])
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(Cadmium['Average_Red_Total/Total_Trx'] , Viability_Cadmium['Average_OD'])
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(Cadmium['Average_Red_Total/Total_Trx'] , Viability_Cadmium['Average_OD'])
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
print('Distance correlation, P-value')
dcor, pval = distance_corr(Cadmium['Average_Red_Total/Total_Trx'], Viability_Cadmium['Average_OD'], seed=None)
print(round(dcor, 3), pval)
print('Potassium ferricyanide')
corr, pvalue = scipy.stats.pearsonr(Cyanide['Average_Red_Total/Total_Trx'] , Viability_Cyanide['Average_OD'])
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(Cyanide['Average_Red_Total/Total_Trx'] , Viability_Cyanide['Average_OD'])
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(Cyanide['Average_Red_Total/Total_Trx'] , Viability_Cyanide['Average_OD'])
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(Cyanide['Average_Red_Total/Total_Trx'] , Viability_Cyanide['Average_OD'])
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
print('Distance correlation, P-value')
dcor, pval = distance_corr(Cyanide['Average_Red_Total/Total_Trx'] , Viability_Cyanide['Average_OD'], seed=None)
print(round(dcor, 3), pval)
plt.errorbar(No_stress['Average_Red_Total/Total_Trx'] , Viability_no_stress['Average_OD'], xerr = No_stress['Std error for Trx redox charge'], yerr = Viability_no_stress['Std error of average OD'], fmt='o', color='royalblue', capsize = 4, label='No stress control', linewidth=2)
plt.errorbar(Heat['Average_Red_Total/Total_Trx'] , Viability_Heat['Average_OD'], xerr = Heat['Std error for Trx redox charge'], yerr = Viability_Heat['Std error of average OD'], fmt='o', color='magenta', capsize = 4, label='50$^\circ$C Heat', linewidth=2)
plt.errorbar(Cadmium['Average_Red_Total/Total_Trx'] , Viability_Cadmium['Average_OD'], xerr = Cadmium['Std error for Trx redox charge'], yerr = Viability_Cadmium['Std error of average OD'], fmt='o', color='limegreen', capsize = 4, label=r'8 mM ${\rm CdSO_4}$', linewidth=2)
plt.errorbar(Cyanide['Average_Red_Total/Total_Trx'] , Viability_Cyanide['Average_OD'], xerr = Cyanide['Std error for Trx redox charge'], yerr = Viability_Cyanide['Std error of average OD'], fmt='o', color='deepskyblue', capsize = 4, label=r'30 mM ${\rm K_3Fe(CN)_6}$', linewidth=2)
plt.scatter(No_stress['Average_Red_Total/Total_Trx'] , Viability_no_stress['Average_OD'], s=100, color = 'royalblue')
plt.scatter(Heat['Average_Red_Total/Total_Trx'] , Viability_Heat['Average_OD'], s=100, color = 'magenta')
plt.scatter(Cadmium['Average_Red_Total/Total_Trx'] , Viability_Cadmium['Average_OD'], s=100, color = 'limegreen')
plt.scatter(Cyanide['Average_Red_Total/Total_Trx'] , Viability_Cyanide['Average_OD'], s=100, color = 'deepskyblue')
plt.xlabel(r'Trx redox charge',size=20, color='black')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='black')
plt.legend(loc='best', frameon=False)
plt.legend(fontsize=18)
plt.xlim([0.3, 1])
plt.ylim([0, 3])
plt.xticks(fontsize=18, color='black')
plt.yticks(fontsize=18,color='black')
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Correlation_Other_stressors.png', dpi=500, bbox_inches='tight')
plt.show()
In [32]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
from scipy.spatial import distance
plt.figure(figsize=(12,9))
plt.errorbar(No_stress['Average_Red_Total/Total_Trx'] , Viability_no_stress['Average_OD'], xerr = No_stress['Std error for Trx redox charge'], yerr = Viability_no_stress['Std error of average OD'], fmt='o', color='royalblue', capsize = 4, label='No stress control', linewidth=2)
plt.errorbar(H2O2_100uM['Average_Red_Total/Total_Trx'] , Viability_100uM_H2O2['Average_OD'], xerr = H2O2_100uM['Std error for Trx redox charge'], yerr = Viability_100uM_H2O2['Std error of average OD'], fmt='o', color='darkorange', capsize = 4, label=r'100 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=2)
plt.errorbar(H2O2_300uM['Average_Red_Total/Total_Trx'] , Viability_300uM_H2O2['Average_OD'], xerr = H2O2_300uM['Std error for Trx redox charge'], yerr = Viability_300uM_H2O2['Std error of average OD'], fmt='o', color='seagreen', capsize = 4, label=r'300 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=2)
plt.errorbar(H2O2_500uM['Average_Red_Total/Total_Trx'] , Viability_500uM_H2O2['Average_OD'], xerr = H2O2_500uM['Std error for Trx redox charge'], yerr = Viability_500uM_H2O2['Std error of average OD'], fmt='o', color='crimson', capsize = 4, label=r'500 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=2)
plt.errorbar(H2O2_1250uM['Average_Red_Total/Total_Trx'] , Viability_1250uM_H2O2['Average_OD'], xerr = H2O2_1250uM['Std error for Trx redox charge'], yerr = Viability_1250uM_H2O2['Std error of average OD'], fmt='o', color='darkviolet', capsize = 4, label=r'1250 ${\rm \mu}$M ${\rm H_2O_2}$', linewidth=2)
plt.errorbar(Heat['Average_Red_Total/Total_Trx'] , Viability_Heat['Average_OD'], xerr = Heat['Std error for Trx redox charge'], yerr = Viability_Heat['Std error of average OD'], fmt='o', color='magenta', capsize = 4, label='50$^\circ$C Heat', linewidth=2)
plt.errorbar(Cadmium['Average_Red_Total/Total_Trx'] , Viability_Cadmium['Average_OD'], xerr = Cadmium['Std error for Trx redox charge'], yerr = Viability_Cadmium['Std error of average OD'], fmt='o', color='limegreen', capsize = 4, label=r'8 mM ${\rm CdSO_4}$', linewidth=2)
plt.errorbar(Cyanide['Average_Red_Total/Total_Trx'] , Viability_Cyanide['Average_OD'], xerr = Cyanide['Std error for Trx redox charge'], yerr = Viability_Cyanide['Std error of average OD'], fmt='o', color='deepskyblue', capsize = 4, label=r'30 mM ${\rm K_3Fe(CN)_6}$', linewidth=2)
plt.scatter(No_stress['Average_Red_Total/Total_Trx'] , Viability_no_stress['Average_OD'], s=100, color = 'royalblue')
plt.scatter(H2O2_100uM['Average_Red_Total/Total_Trx'] , Viability_100uM_H2O2['Average_OD'], s=100, color = 'darkorange')
plt.scatter(H2O2_300uM['Average_Red_Total/Total_Trx'] , Viability_300uM_H2O2['Average_OD'], s=100, color = 'seagreen')
plt.scatter(H2O2_500uM['Average_Red_Total/Total_Trx'] , Viability_500uM_H2O2['Average_OD'], s=100, color = 'crimson')
plt.scatter(H2O2_1250uM['Average_Red_Total/Total_Trx'] , Viability_1250uM_H2O2['Average_OD'], s=100, color = 'darkviolet')
plt.scatter(Heat['Average_Red_Total/Total_Trx'] , Viability_Heat['Average_OD'], s=100, color = 'magenta')
plt.scatter(Cadmium['Average_Red_Total/Total_Trx'] , Viability_Cadmium['Average_OD'], s=100, color = 'limegreen')
plt.scatter(Cyanide['Average_Red_Total/Total_Trx'] , Viability_Cyanide['Average_OD'], s=100, color = 'deepskyblue')
plt.xlabel(r'Trx redox charge',size=20, color='black')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='black')
plt.legend(loc='best', frameon=False)
plt.legend(fontsize=18)
plt.xlim([0.3, 1])
plt.ylim([0, 3])
plt.xticks(fontsize=18, color='black')
plt.yticks(fontsize=18,color='black')
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Correlation_All stressors.png', dpi=500, bbox_inches='tight')
plt.show()
Section S6 - Total protein (Ponceau S stain) analyses¶
In [96]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('No stress_Ponceau_1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('No stress_Ponceau_2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('No stress_Ponceau_3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
No stress control - Ponceau S (Total protein) analysis¶
In [69]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\No stress_Ponceau.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\No stress_Ponceau.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\No stress_Ponceau.csv')
No_stress_Pon = pd.read_csv('No stress_Ponceau.csv')
No_stress_Pon["R1_Red_Total/(R1_Total protein)"] = (No_stress_Pon['R1_RedA'] + No_stress_Pon['R1_RedB']).div(No_stress_Pon['R1_Total_protein'])
No_stress_Pon["R2_Red_Total/(R2_Total protein)"] = (No_stress_Pon['R2_RedA'] + No_stress_Pon['R2_RedB']).div(No_stress_Pon['R2_Total_protein'])
No_stress_Pon["R3_Red_Total/(R3_Total potein)"] = (No_stress_Pon['R3_RedA'] + No_stress_Pon['R3_RedB']).div(No_stress_Pon['R3_Total_protein'])
No_stress_Pon['Average_Red_Total/Total_protein'] = No_stress_Pon.iloc[:, [10,11,12]].mean(axis=1)
No_stress_Pon['Std error for Red total Trx/Total protein'] = No_stress_Pon.iloc[:, [10,11,12]].sem(axis=1)
No_stress_Pon
Out[69]:
In [70]:
fig, ax1 = plt.subplots()
ax2=ax1.twinx()
ax3=ax1.twinx()
ax1 = No_stress.plot('Time (min)','Average_Red_Total/Total_Trx', color = 'green', ax=ax1, figsize=(9,6), marker = 'o')
ax1.set_ylabel('Trx redox charge', color = 'green', fontsize=18)
ax1.set_ylim(0.5,1)
ax1.tick_params(axis = 'y', colors = 'green', labelsize=18)
ax3.spines['left'].set_color('green')
x1 = No_stress['Time (min)']
y1 = No_stress['Average_Red_Total/Total_Trx']
y1error = No_stress['Std error for Trx redox charge']
x1error = No_stress['Std error for Trx redox charge']
ax1.errorbar(x1,y1,yerr=y1error,xerr=x1error, color='green', capsize=3)
ax2 = Viability_no_stress.plot('Time (min)','Average_OD', color = 'blue', ax=ax2, marker = 'o')
ax2.set_ylabel('Absorbance (562 nm)', color = 'blue', fontsize=18)
ax2.set_ylim(1,2)
ax2.tick_params(axis = 'y', colors = 'blue', labelsize=18)
ax2.spines['right'].set_color('blue')
x2 = Viability_no_stress['Time (min)']
y2 = Viability_no_stress['Average_OD']
y2error = Viability_no_stress['Std error of average OD']
x2error = Viability_no_stress['Std error of average OD']
ax2.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='blue', capsize=3)
ax3 = No_stress_Pon.plot('Time (min)','Average_Red_Total/Total_protein', color = 'red', ax=ax3, marker = 'o')
ax3.set_ylabel('Reduced Trx/Total protein', color = 'red', fontsize=18)
ax3.set_ylim(0.2,0.6)
ax3.tick_params(axis = 'y', colors = 'red', labelsize=18)
ax3.spines['right'].set_position(('outward', 80))
ax3.spines['right'].set_color('red')
x3 = No_stress_Pon['Time (min)']
y3 = No_stress_Pon['Average_Red_Total/Total_protein']
y3error = No_stress_Pon['Std error for Red total Trx/Total protein']
x3error = No_stress_Pon['Std error for Red total Trx/Total protein']
ax3.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='red', capsize=3)
ax1.set_xlabel('Time (min)', color = 'Black', fontsize=18)
ax1.tick_params(axis='x', labelsize=18)
ax1.get_legend().remove()
ax2.get_legend().remove()
ax3.get_legend().remove()
plt.plot()
plt.savefig('No stress control triple plot.png', dpi=500, bbox_inches='tight')
In [102]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = No_stress_Pon['Average_Red_Total/Total_protein']
y = No_stress['Average_Red_Total/Total_Trx']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = No_stress_Pon['Std error for Red total Trx/Total protein']
yerr = No_stress['Std error for Trx redox charge']
plt.errorbar(x, y, xerr = No_stress_Pon['Std error for Red total Trx/Total protein'], yerr = No_stress['Std error for Trx redox charge'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Trx redox charge',size=20, color='green')
plt.legend(['No stress control'], frameon=False)
plt.xlim([0, 0.5])
plt.ylim([0.5, 1])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: -0.574$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: -0.071$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: -0.048$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.330$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Ponceau_correlation_No stress.png', dpi=500, bbox_inches='tight')
plt.show()
In [71]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = No_stress_Pon['Average_Red_Total/Total_protein']
y = Viability_no_stress['Average_OD']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = No_stress_Pon['Std error for Red total Trx/Total protein']
yerr = Viability_no_stress['Std error of average OD']
plt.errorbar(x, y, xerr = No_stress_Pon['Std error for Red total Trx/Total protein'], yerr = Viability_no_stress['Std error of average OD'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='blue')
plt.legend(['No stress control'], frameon=False)
plt.xlim([0, 0.5])
plt.ylim([0, 2])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.457$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: 0.500$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: 0.429$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.209$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Viability vs total protein_No stress.png', dpi=500, bbox_inches='tight')
plt.show()
In [105]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('100uM_H2O2_Pon1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('100uM_H2O2_Pon2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('100uM_H2O2_Pon3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
100 $\mu$M Hydrogen peroxide - Ponceau S (Total protein) analysis¶
In [72]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\100 uM H2O2 Pon.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\100 uM H2O2 Pon.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\100 uM H2O2 Pon.csv')
H2O2_100uM_Pon = pd.read_csv('100 uM H2O2 Pon.csv')
H2O2_100uM_Pon["R1_Red_Total/(R1_Total protein)"] = (H2O2_100uM_Pon['R1_RedA'] + H2O2_100uM_Pon['R1_RedB']).div(H2O2_100uM_Pon['R1_Total_protein'])
H2O2_100uM_Pon["R2_Red_Total/(R2_Total protein)"] = (H2O2_100uM_Pon['R2_RedA'] + H2O2_100uM_Pon['R2_RedB']).div(H2O2_100uM_Pon['R2_Total_protein'])
H2O2_100uM_Pon["R3_Red_Total/(R3_Total potein)"] = (H2O2_100uM_Pon['R3_RedA'] + H2O2_100uM_Pon['R3_RedB']).div(H2O2_100uM_Pon['R3_Total_protein'])
H2O2_100uM_Pon['Average_Red_Total/Total_protein'] = H2O2_100uM_Pon.iloc[:, [10,11,12]].mean(axis=1)
H2O2_100uM_Pon['Std error for Red total Trx/Total protein'] = H2O2_100uM_Pon.iloc[:, [10,11,12]].sem(axis=1)
H2O2_100uM_Pon
Out[72]:
In [73]:
fig, ax1 = plt.subplots()
ax2=ax1.twinx()
ax3=ax1.twinx()
ax1 = H2O2_100uM.plot('Time (min)','Average_Red_Total/Total_Trx', color = 'green', ax=ax1, figsize=(9,6), marker = 'o')
ax1.set_ylabel('Trx redox charge', color = 'green', fontsize=18)
ax1.set_ylim(0.3,1)
ax1.tick_params(axis = 'y', colors = 'green', labelsize=18)
ax3.spines['left'].set_color('green')
x1 = H2O2_100uM['Time (min)']
y1 = H2O2_100uM['Average_Red_Total/Total_Trx']
y1error = H2O2_100uM['Std error for Trx redox charge']
x1error = H2O2_100uM['Std error for Trx redox charge']
ax1.errorbar(x1,y1,yerr=y1error,xerr=x1error, color='green', capsize=3)
ax2 = Viability_100uM_H2O2.plot('Time (min)','Average_OD', color = 'blue', ax=ax2, marker = 'o')
ax2.set_ylabel('Absorbance (562 nm)', color = 'blue', fontsize=18)
ax2.set_ylim(0.5,2)
ax2.tick_params(axis = 'y', colors = 'blue', labelsize=18)
ax2.spines['right'].set_color('blue')
x2 = Viability_100uM_H2O2['Time (min)']
y2 = Viability_100uM_H2O2['Average_OD']
y2error = Viability_100uM_H2O2['Std error of average OD']
x2error = Viability_100uM_H2O2['Std error of average OD']
ax2.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='blue', capsize=3)
ax3 = H2O2_100uM_Pon.plot('Time (min)','Average_Red_Total/Total_protein', color = 'red', ax=ax3, marker = 'o')
ax3.set_ylabel('Reduced Trx/Total protein', color = 'red', fontsize=18)
ax3.set_ylim(0,0.6)
ax3.tick_params(axis = 'y', colors = 'red', labelsize=18)
ax3.spines['right'].set_position(('outward', 80))
ax3.spines['right'].set_color('red')
x3 = H2O2_100uM_Pon['Time (min)']
y3 = H2O2_100uM_Pon['Average_Red_Total/Total_protein']
y3error = H2O2_100uM_Pon['Std error for Red total Trx/Total protein']
x3error = H2O2_100uM_Pon['Std error for Red total Trx/Total protein']
ax3.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='red', capsize=3)
ax1.set_xlabel('Time (min)', color = 'Black', fontsize=18)
ax1.tick_params(axis='x', labelsize=18)
ax1.get_legend().remove()
ax2.get_legend().remove()
ax3.get_legend().remove()
plt.plot()
plt.savefig('100 uM H2O2_triple plot.png', dpi=500, bbox_inches='tight')
In [110]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = H2O2_100uM_Pon['Average_Red_Total/Total_protein']
y = H2O2_100uM['Average_Red_Total/Total_Trx']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = H2O2_100uM_Pon['Std error for Red total Trx/Total protein']
yerr = H2O2_100uM['Std error for Trx redox charge']
plt.errorbar(x, y, xerr = H2O2_100uM_Pon['Std error for Red total Trx/Total protein'], yerr = H2O2_100uM['Std error for Trx redox charge'] , fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20,color='red')
plt.ylabel(r'Trx redox charge',size=20,color='green')
plt.legend(['100 $\mu$M Hydrogen peroxide'], frameon=False)
plt.xlim([0, 0.5])
plt.ylim([0, 1.2])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.959$^{***}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: 0.857$^{*}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: 0.714$^{*}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.920$^{***}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Ponceau_correlation_100 uM H2O2.png', dpi=500, bbox_inches='tight')
plt.show()
In [74]:
import matplotlib.pyplot as plt
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = H2O2_100uM_Pon['Average_Red_Total/Total_protein']
y = Viability_100uM_H2O2['Average_OD']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = H2O2_100uM_Pon['Std error for Red total Trx/Total protein']
yerr = Viability_100uM_H2O2['Std error of average OD']
plt.errorbar(x, y, xerr = H2O2_100uM_Pon['Std error for Red total Trx/Total protein'], yerr = Viability_100uM_H2O2['Std error of average OD'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='blue')
plt.legend(['100 $\mu$M Hydrogen peroxide'], frameon=False)
plt.xlim([0, 0.5])
plt.ylim([0, 2])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.742$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: 0.679$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: 0.524$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.551$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Viability vs total protein_100 uM H2O2.png', dpi=500, bbox_inches='tight')
plt.show()
In [123]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('300uM_H2O2_Pon1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('300uM_H2O2_Pon2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('300uM_H2O2_Pon3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
300 $\mu$M Hydrogen peroxide - Ponceau S (Total protein) analysis¶
In [75]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\300 uM H2O2 Pon.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\300 uM H2O2 Pon.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\300 uM H2O2 Pon.csv')
H2O2_300uM_Pon = pd.read_csv('300 uM H2O2 Pon.csv')
H2O2_300uM_Pon["R1_Red_Total/(R1_Total protein)"] = (H2O2_300uM_Pon['R1_RedA'] + H2O2_300uM_Pon['R1_RedB']).div(H2O2_300uM_Pon['R1_Total_protein'])
H2O2_300uM_Pon["R2_Red_Total/(R2_Total protein)"] = (H2O2_300uM_Pon['R2_RedA'] + H2O2_300uM_Pon['R2_RedB']).div(H2O2_300uM_Pon['R2_Total_protein'])
H2O2_300uM_Pon["R3_Red_Total/(R3_Total potein)"] = (H2O2_300uM_Pon['R3_RedA'] + H2O2_300uM_Pon['R3_RedB']).div(H2O2_300uM_Pon['R3_Total_protein'])
H2O2_300uM_Pon['Average_Red_Total/Total_protein'] = H2O2_300uM_Pon.iloc[:, [10,11,12]].mean(axis=1)
H2O2_300uM_Pon['Std error for Red total Trx/Total protein'] = H2O2_300uM_Pon.iloc[:, [10,11,12]].sem(axis=1)
H2O2_300uM_Pon
Out[75]:
In [76]:
fig, ax1 = plt.subplots()
ax2=ax1.twinx()
ax3=ax1.twinx()
ax1 = H2O2_300uM.plot('Time (min)','Average_Red_Total/Total_Trx', color = 'green', ax=ax1, figsize=(9,6), marker = 'o')
ax1.set_ylabel('Trx redox charge', color = 'green', fontsize=18)
ax1.set_ylim(0.5,1)
ax1.tick_params(axis = 'y', colors = 'green', labelsize=18)
ax3.spines['left'].set_color('green')
x1 = H2O2_300uM['Time (min)']
y1 = H2O2_300uM['Average_Red_Total/Total_Trx']
y1error = H2O2_300uM['Std error for Trx redox charge']
x1error = H2O2_300uM['Std error for Trx redox charge']
ax1.errorbar(x1,y1,yerr=y1error,xerr=x1error, color='green', capsize=3)
ax2 = Viability_300uM_H2O2.plot('Time (min)','Average_OD', color = 'blue', ax=ax2, marker = 'o')
ax2.set_ylabel('Absorbance (562 nm)', color = 'blue', fontsize=18)
ax2.set_ylim(0.6,1.1)
ax2.tick_params(axis = 'y', colors = 'blue', labelsize=18)
ax2.spines['right'].set_color('blue')
x2 = Viability_300uM_H2O2['Time (min)']
y2 = Viability_300uM_H2O2['Average_OD']
y2error = Viability_300uM_H2O2['Std error of average OD']
x2error = Viability_300uM_H2O2['Std error of average OD']
ax2.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='blue', capsize=3)
ax3 = H2O2_300uM_Pon.plot('Time (min)','Average_Red_Total/Total_protein', color = 'red', ax=ax3, marker = 'o')
ax3.set_ylabel('Reduced Trx/Total protein', color = 'red', fontsize=18)
ax3.set_ylim(0,0.8)
ax3.tick_params(axis = 'y', colors = 'red', labelsize=18)
ax3.spines['right'].set_position(('outward', 80))
ax3.spines['right'].set_color('red')
x3 = H2O2_300uM_Pon['Time (min)']
y3 = H2O2_300uM_Pon['Average_Red_Total/Total_protein']
y3error = H2O2_300uM_Pon['Std error for Red total Trx/Total protein']
x3error = H2O2_300uM_Pon['Std error for Red total Trx/Total protein']
ax3.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='red', capsize=3)
ax1.set_xlabel('Time (min)', color = 'Black', fontsize=18)
ax1.tick_params(axis='x', labelsize=18)
ax1.get_legend().remove()
ax2.get_legend().remove()
ax3.get_legend().remove()
plt.plot()
plt.savefig('300 uM H2O2_triple plot.png', dpi=500, bbox_inches='tight')
In [127]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = H2O2_300uM_Pon['Average_Red_Total/Total_protein']
y = H2O2_300uM['Average_Red_Total/Total_Trx']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = H2O2_300uM_Pon['Std error for Red total Trx/Total protein']
yerr = H2O2_300uM['Std error for Trx redox charge']
plt.errorbar(x, y, xerr = H2O2_300uM_Pon['Std error for Red total Trx/Total protein'], yerr = H2O2_300uM['Std error for Trx redox charge'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Trx redox charge',size=20,color='green')
plt.legend(['300 $\mu$M Hydrogen peroxide'], frameon=False)
plt.xlim([0, 0.8])
plt.ylim([0.3, 1])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: -0.033$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: -0.143$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: -0.238$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.001$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Ponceau_correlation_300 uM H2O2.png', dpi=500, bbox_inches='tight')
plt.show()
In [77]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = H2O2_300uM_Pon['Average_Red_Total/Total_protein']
y = Viability_300uM_H2O2['Average_OD']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = H2O2_300uM_Pon['Std error for Red total Trx/Total protein']
yerr = Viability_300uM_H2O2['Std error of average OD']
plt.errorbar(x, y, xerr = H2O2_300uM_Pon['Std error for Red total Trx/Total protein'], yerr = Viability_300uM_H2O2['Std error of average OD'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='blue')
plt.legend(['300 $\mu$M Hydrogen peroxide'], frameon=False)
plt.xlim([0.2, 0.8])
plt.ylim([0, 1.5])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: -0.591$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: -0.321$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: -0.238$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.349$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Viability vs total protein_300 uM H2O2.png', dpi=500, bbox_inches='tight')
plt.show()
In [130]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('500uM_H2O2_Pon1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('500uM_H2O2_Pon2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('500uM_H2O2_Pon3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
500 $\mu$M Hydrogen peroxide - Ponceau S (Total protein) analysis¶
In [78]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\500 uM H2O2 Pon.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\500 uM H2O2 Pon.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\500 uM H2O2 Pon.csv')
H2O2_500uM_Pon = pd.read_csv('500 uM H2O2 Pon.csv')
H2O2_500uM_Pon["R1_Red_Total/(R1_Total protein)"] = (H2O2_500uM_Pon['R1_RedA'] + H2O2_500uM_Pon['R1_RedB']).div(H2O2_500uM_Pon['R1_Total_protein'])
H2O2_500uM_Pon["R2_Red_Total/(R2_Total protein)"] = (H2O2_500uM_Pon['R2_RedA'] + H2O2_500uM_Pon['R2_RedB']).div(H2O2_500uM_Pon['R2_Total_protein'])
H2O2_500uM_Pon["R3_Red_Total/(R3_Total potein)"] = (H2O2_500uM_Pon['R3_RedA'] + H2O2_500uM_Pon['R3_RedB']).div(H2O2_500uM_Pon['R3_Total_protein'])
H2O2_500uM_Pon['Average_Red_Total/Total_protein'] = H2O2_500uM_Pon.iloc[:, [10,11,12]].mean(axis=1)
H2O2_500uM_Pon['Std error for Red total Trx/Total protein'] = H2O2_500uM_Pon.iloc[:, [10,11,12]].sem(axis=1)
H2O2_500uM_Pon
Out[78]:
In [79]:
fig, ax1 = plt.subplots()
ax2=ax1.twinx()
ax3=ax1.twinx()
ax1 = H2O2_500uM.plot('Time (min)','Average_Red_Total/Total_Trx', color = 'green', ax=ax1, figsize=(9,6), marker = 'o')
ax1.set_ylabel('Trx redox charge', color = 'green', fontsize=18)
ax1.set_ylim(0.5,1)
ax1.tick_params(axis = 'y', colors = 'green', labelsize=18)
ax3.spines['left'].set_color('green')
x1 = H2O2_500uM['Time (min)']
y1 = H2O2_500uM['Average_Red_Total/Total_Trx']
y1error = H2O2_500uM['Std error for Trx redox charge']
x1error = H2O2_500uM['Std error for Trx redox charge']
ax1.errorbar(x1,y1,yerr=y1error,xerr=x1error, color='green', capsize=3)
ax2 = Viability_500uM_H2O2.plot('Time (min)','Average_OD', color = 'blue', ax=ax2, marker = 'o')
ax2.set_ylabel('Absorbance (562 nm)', color = 'blue', fontsize=18)
ax2.set_ylim(0.2,1.2)
ax2.tick_params(axis = 'y', colors = 'blue', labelsize=18)
ax2.spines['right'].set_color('blue')
x2 = Viability_500uM_H2O2['Time (min)']
y2 = Viability_500uM_H2O2['Average_OD']
y2error = Viability_500uM_H2O2['Std error of average OD']
x2error = Viability_500uM_H2O2['Std error of average OD']
ax2.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='blue', capsize=3)
ax3 = H2O2_500uM_Pon.plot('Time (min)','Average_Red_Total/Total_protein', color = 'red', ax=ax3, marker = 'o')
ax3.set_ylabel('Reduced Trx/Total protein', color = 'red', fontsize=18)
ax3.set_ylim(0,0.6)
ax3.tick_params(axis = 'y', colors = 'red', labelsize=18)
ax3.spines['right'].set_position(('outward', 80))
ax3.spines['right'].set_color('red')
x3 = H2O2_500uM_Pon['Time (min)']
y3 = H2O2_500uM_Pon['Average_Red_Total/Total_protein']
y3error = H2O2_500uM_Pon['Std error for Red total Trx/Total protein']
x3error = H2O2_500uM_Pon['Std error for Red total Trx/Total protein']
ax3.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='red', capsize=3)
ax1.set_xlabel('Time (min)', color = 'Black', fontsize=18)
ax1.tick_params(axis='x', labelsize=18)
ax1.get_legend().remove()
ax2.get_legend().remove()
ax3.get_legend().remove()
plt.plot()
plt.savefig('500 uM H2O2_triple plot.png', dpi=500, bbox_inches='tight')
In [134]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = H2O2_500uM_Pon['Average_Red_Total/Total_protein']
y = H2O2_500uM['Average_Red_Total/Total_Trx']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = H2O2_500uM_Pon['Std error for Red total Trx/Total protein']
yerr = H2O2_500uM['Std error for Trx redox charge']
plt.errorbar(x, y, xerr = H2O2_500uM_Pon['Std error for Red total Trx/Total protein'] , yerr = H2O2_500uM['Std error for Trx redox charge'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20,color='red')
plt.ylabel(r'Trx redox charge',size=20,color='green')
plt.legend(['500 $\mu$M Hydrogen peroxide'], frameon=False)
plt.xlim([0.2, 0.7])
plt.ylim([0, 1])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.351$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: 0.571$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: 0.429$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.123$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Ponceau_correlation_500 uM H2O2.png', dpi=500, bbox_inches='tight')
plt.show()
In [80]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = H2O2_500uM_Pon['Average_Red_Total/Total_protein']
y = Viability_500uM_H2O2['Average_OD']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = H2O2_500uM_Pon['Std error for Red total Trx/Total protein']
yerr = Viability_500uM_H2O2['Std error of average OD']
plt.errorbar(x, y, xerr = H2O2_500uM_Pon['Std error for Red total Trx/Total protein'], yerr = Viability_500uM_H2O2['Std error of average OD'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='blue')
plt.legend(['500 $\mu$M Hydrogen peroxide'], frameon=False)
plt.xlim([0.2, 0.7])
plt.ylim([0, 1.5])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.395$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: -0.214$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: -0.143$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.156$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Viability vs total protein_500 uM H2O2.png', dpi=500, bbox_inches='tight')
plt.show()
In [137]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('1250uM_H2O2_Pon1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('1250uM_H2O2_Pon2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('1250uM_H2O2_Pon3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
1250 $\mu$M Hydrogen peroxide - Ponceau S (Total protein) analysis¶
In [81]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\1250 uM H2O2 Pon.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\1250 uM H2O2 Pon.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\1250 uM H2O2 Pon.csv')
H2O2_1250uM_Pon = pd.read_csv('1250 uM H2O2 Pon.csv')
H2O2_1250uM_Pon["R1_Red_Total/(R1_Total protein)"] = (H2O2_1250uM_Pon['R1_RedA'] + H2O2_1250uM_Pon['R1_RedB']).div(H2O2_1250uM_Pon['R1_Total_protein'])
H2O2_1250uM_Pon["R2_Red_Total/(R2_Total protein)"] = (H2O2_1250uM_Pon['R2_RedA'] + H2O2_1250uM_Pon['R2_RedB']).div(H2O2_1250uM_Pon['R2_Total_protein'])
H2O2_1250uM_Pon["R3_Red_Total/(R3_Total potein)"] = (H2O2_1250uM_Pon['R3_RedA'] + H2O2_1250uM_Pon['R3_RedB']).div(H2O2_1250uM_Pon['R3_Total_protein'])
H2O2_1250uM_Pon['Average_Red_Total/Total_protein'] = H2O2_1250uM_Pon.iloc[:, [10,11,12]].mean(axis=1)
H2O2_1250uM_Pon['Std error for Red total Trx/Total protein'] = H2O2_1250uM_Pon.iloc[:, [10,11,12]].sem(axis=1)
H2O2_1250uM_Pon
Out[81]:
In [82]:
fig, ax1 = plt.subplots()
ax2=ax1.twinx()
ax3=ax1.twinx()
ax1 = H2O2_1250uM.plot('Time (min)','Average_Red_Total/Total_Trx', color = 'green', ax=ax1, figsize=(9,6), marker = 'o')
ax1.set_ylabel('Trx redox charge', color = 'green', fontsize=18)
ax1.set_ylim(0.5,1)
ax1.tick_params(axis = 'y', colors = 'green', labelsize=18)
ax3.spines['left'].set_color('green')
x1 = H2O2_1250uM['Time (min)']
y1 = H2O2_1250uM['Average_Red_Total/Total_Trx']
y1error = H2O2_1250uM['Std error for Trx redox charge']
x1error = H2O2_1250uM['Std error for Trx redox charge']
ax1.errorbar(x1,y1,yerr=y1error,xerr=x1error, color='green', capsize=3)
ax2 = Viability_1250uM_H2O2.plot('Time (min)','Average_OD', color = 'blue', ax=ax2, marker = 'o')
ax2.set_ylabel('Absorbance (562 nm)', color = 'blue', fontsize=18)
ax2.set_ylim(0,3)
ax2.tick_params(axis = 'y', colors = 'blue', labelsize=18)
ax2.spines['right'].set_color('blue')
x2 = Viability_1250uM_H2O2['Time (min)']
y2 = Viability_1250uM_H2O2['Average_OD']
y2error = Viability_1250uM_H2O2['Std error of average OD']
x2error = Viability_1250uM_H2O2['Std error of average OD']
ax2.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='blue', capsize=3)
ax3 = H2O2_1250uM_Pon.plot('Time (min)','Average_Red_Total/Total_protein', color = 'red', ax=ax3, marker = 'o')
ax3.set_ylabel('Reduced Trx/Total protein', color = 'red', fontsize=18)
ax3.set_ylim(0.1,0.7)
ax3.tick_params(axis = 'y', colors = 'red', labelsize=18)
ax3.spines['right'].set_position(('outward', 80))
ax3.spines['right'].set_color('red')
x3 = H2O2_1250uM_Pon['Time (min)']
y3 = H2O2_1250uM_Pon['Average_Red_Total/Total_protein']
y3error = H2O2_1250uM_Pon['Std error for Red total Trx/Total protein']
x3error = H2O2_1250uM_Pon['Std error for Red total Trx/Total protein']
ax3.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='red', capsize=3)
ax1.set_xlabel('Time (min)', color = 'Black', fontsize=18)
ax1.tick_params(axis='x', labelsize=18)
ax1.get_legend().remove()
ax2.get_legend().remove()
ax3.get_legend().remove()
plt.plot()
plt.savefig('1250 uM H2O2_triple plot.png', dpi=500, bbox_inches='tight')
In [141]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = H2O2_1250uM_Pon['Average_Red_Total/Total_protein']
y = H2O2_1250uM['Average_Red_Total/Total_Trx']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = H2O2_1250uM_Pon['Std error for Red total Trx/Total protein']
yerr = H2O2_1250uM['Std error for Trx redox charge']
plt.errorbar(x, y, xerr = H2O2_1250uM_Pon['Std error for Red total Trx/Total protein'] , yerr = H2O2_1250uM['Std error for Trx redox charge'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Trx redox charge',size=20, color='green')
plt.legend(['1250 $\mu$M Hydrogen peroxide'], frameon=False)
plt.xlim([0, 0.7])
plt.ylim([0, 1])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.746$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: 0.643$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: 0.429$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.557$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Ponceau_correlation_1250 uM H2O2.png', dpi=500, bbox_inches='tight')
plt.show()
In [83]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = H2O2_1250uM_Pon['Average_Red_Total/Total_protein']
y = Viability_1250uM_H2O2['Average_OD']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = H2O2_1250uM_Pon['Std error for Red total Trx/Total protein']
yerr = Viability_1250uM_H2O2['Std error of average OD']
plt.errorbar(x, y, xerr = H2O2_1250uM_Pon['Std error for Red total Trx/Total protein'], yerr = Viability_1250uM_H2O2['Std error of average OD'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='blue')
plt.legend(['1250 $\mu$M Hydrogen peroxide'], frameon=False)
plt.xlim([0, 0.7])
plt.ylim([0, 3])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.272$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: 0.750$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: 0.524$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.074$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Viability vs total protein_1250 uM H2O2.png', dpi=500, bbox_inches='tight')
plt.show()
In [145]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Heat_Pon1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Heat_Pon2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Heat_Pon3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
50$^\circ$C Heat - Ponceau S (Total protein) analysis¶
In [84]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Heat Pon.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Heat Pon.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Heat Pon.csv')
Heat_Pon = pd.read_csv('Heat Pon.csv')
Heat_Pon["R1_Red_Total/(R1_Total protein)"] = (Heat_Pon['R1_RedA'] + Heat_Pon['R1_RedB']).div(Heat_Pon['R1_Total_protein'])
Heat_Pon["R2_Red_Total/(R2_Total protein)"] = (Heat_Pon['R2_RedA'] + Heat_Pon['R2_RedB']).div(Heat_Pon['R2_Total_protein'])
Heat_Pon["R3_Red_Total/(R3_Total potein)"] = (Heat_Pon['R3_RedA'] + Heat_Pon['R3_RedB']).div(Heat_Pon['R3_Total_protein'])
Heat_Pon['Average_Red_Total/Total_protein'] = Heat_Pon.iloc[:, [10,11,12]].mean(axis=1)
Heat_Pon['Std error for Red total Trx/Total protein'] = Heat_Pon.iloc[:, [10,11,12]].sem(axis=1)
Heat_Pon
Out[84]:
In [85]:
fig, ax1 = plt.subplots()
ax2=ax1.twinx()
ax3=ax1.twinx()
ax1 = Heat.plot('Time (min)','Average_Red_Total/Total_Trx', color = 'green', ax=ax1, figsize=(9,6), marker = 'o')
ax1.set_ylabel('Trx redox charge', color = 'green', fontsize=18)
ax1.set_ylim(0.6,1)
ax1.tick_params(axis = 'y', colors = 'green', labelsize=18)
ax3.spines['left'].set_color('green')
x1 = Heat['Time (min)']
y1 = Heat['Average_Red_Total/Total_Trx']
y1error = Heat['Std error for Trx redox charge']
x1error = Heat['Std error for Trx redox charge']
ax1.errorbar(x1,y1,yerr=y1error,xerr=x1error, color='green', capsize=3)
ax2 = Viability_Heat.plot('Time (min)','Average_OD', color = 'blue', ax=ax2, marker = 'o')
ax2.set_ylabel('Absorbance (562 nm)', color = 'blue', fontsize=18)
ax2.set_ylim(0.4,1.3)
ax2.tick_params(axis = 'y', colors = 'blue', labelsize=18)
ax2.spines['right'].set_color('blue')
x2 = Viability_Heat['Time (min)']
y2 = Viability_Heat['Average_OD']
y2error = Viability_Heat['Std error of average OD']
x2error = Viability_Heat['Std error of average OD']
ax2.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='blue', capsize=3)
ax3 = Heat_Pon.plot('Time (min)','Average_Red_Total/Total_protein', color = 'red', ax=ax3, marker = 'o')
ax3.set_ylabel('Reduced Trx/Total protein', color = 'red', fontsize=18)
ax3.set_ylim(0.1,0.5)
ax3.tick_params(axis = 'y', colors = 'red', labelsize=18)
ax3.spines['right'].set_position(('outward', 80))
ax3.spines['right'].set_color('red')
x3 = Heat_Pon['Time (min)']
y3 = Heat_Pon['Average_Red_Total/Total_protein']
y3error = Heat_Pon['Std error for Red total Trx/Total protein']
x3error = Heat_Pon['Std error for Red total Trx/Total protein']
ax3.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='red', capsize=3)
ax1.set_xlabel('Time (min)', color = 'Black', fontsize=18)
ax1.tick_params(axis='x', labelsize=18)
ax1.get_legend().remove()
ax2.get_legend().remove()
ax3.get_legend().remove()
plt.plot()
plt.savefig('Heat_triple plot.png', dpi=500, bbox_inches='tight')
In [149]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = Heat_Pon['Average_Red_Total/Total_protein']
y = Heat['Average_Red_Total/Total_Trx']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = Heat_Pon['Std error for Red total Trx/Total protein']
yerr = Heat['Std error for Trx redox charge']
plt.errorbar(x, y, xerr = Heat_Pon['Std error for Red total Trx/Total protein'] , yerr = Heat['Std error for Trx redox charge'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Trx redox charge',size=20, color='green')
plt.legend(['50$^\circ$C Heat'], frameon=False)
plt.xlim([0, 0.5])
plt.ylim([0, 1])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.486$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: 0.429$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: 0.333$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.237$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Ponceau_correlation_Heat.png', dpi=500, bbox_inches='tight')
plt.show()
In [86]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = Heat_Pon['Average_Red_Total/Total_protein']
y = Viability_Heat['Average_OD']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = Heat_Pon['Std error for Red total Trx/Total protein']
yerr = Viability_Heat['Std error of average OD']
plt.errorbar(x, y, xerr = Heat_Pon['Std error for Red total Trx/Total protein'], yerr = Viability_Heat['Std error of average OD'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='blue')
plt.legend(['50$^\circ$C Heat'], frameon=False)
plt.xlim([0, 0.5])
plt.ylim([0, 1.5])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.591$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: 0.357$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: 0.333$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.349$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Viability vs total protein_Heat.png', dpi=500, bbox_inches='tight')
plt.show()
In [152]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cadmium_Pon1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cadmium_Pon2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cadmium_Pon3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
8 mM Cadmium sulfate - Ponceau S (Total protein) analysis¶
In [87]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Cadmium Pon.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Cadmium Pon.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Cadmium Pon.csv')
Cadmium_Pon = pd.read_csv('Cadmium Pon.csv')
Cadmium_Pon["R1_Red_Total/(R1_Total protein)"] = (Cadmium_Pon['R1_RedA'] + Cadmium_Pon['R1_RedB']).div(Cadmium_Pon['R1_Total_protein'])
Cadmium_Pon["R2_Red_Total/(R2_Total protein)"] = (Cadmium_Pon['R2_RedA'] + Cadmium_Pon['R2_RedB']).div(Cadmium_Pon['R2_Total_protein'])
Cadmium_Pon["R3_Red_Total/(R3_Total potein)"] = (Cadmium_Pon['R3_RedA'] + Cadmium_Pon['R3_RedB']).div(Cadmium_Pon['R3_Total_protein'])
Cadmium_Pon['Average_Red_Total/Total_protein'] = Cadmium_Pon.iloc[:, [10,11,12]].mean(axis=1)
Cadmium_Pon['Std error for Red total Trx/Total protein'] = Cadmium_Pon.iloc[:, [10,11,12]].sem(axis=1)
Cadmium_Pon
Out[87]:
In [88]:
fig, ax1 = plt.subplots()
ax2=ax1.twinx()
ax3=ax1.twinx()
ax1 = Cadmium.plot('Time (min)','Average_Red_Total/Total_Trx', color = 'green', ax=ax1, figsize=(9,6), marker = 'o')
ax1.set_ylabel('Trx redox charge', color = 'green', fontsize=18)
ax1.set_ylim(0.6,1)
ax1.tick_params(axis = 'y', colors = 'green', labelsize=18)
ax3.spines['left'].set_color('green')
x1 = Cadmium['Time (min)']
y1 = Cadmium['Average_Red_Total/Total_Trx']
y1error = Cadmium['Std error for Trx redox charge']
x1error = Cadmium['Std error for Trx redox charge']
ax1.errorbar(x1,y1,yerr=y1error,xerr=x1error, color='green', capsize=3)
ax2 = Viability_Cadmium.plot('Time (min)','Average_OD', color = 'blue', ax=ax2, marker = 'o')
ax2.set_ylabel('Absorbance (562 nm)', color = 'blue', fontsize=18)
ax2.set_ylim(0,2.5)
ax2.tick_params(axis = 'y', colors = 'blue', labelsize=18)
ax2.spines['right'].set_color('blue')
x2 = Viability_Cadmium['Time (min)']
y2 = Viability_Cadmium['Average_OD']
y2error = Viability_Cadmium['Std error of average OD']
x2error = Viability_Cadmium['Std error of average OD']
ax2.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='blue', capsize=3)
ax3 = Cadmium_Pon.plot('Time (min)','Average_Red_Total/Total_protein', color = 'red', ax=ax3, marker = 'o')
ax3.set_ylabel('Reduced Trx/Total protein', color = 'red', fontsize=18)
ax3.set_ylim(0.1,0.5)
ax3.tick_params(axis = 'y', colors = 'red', labelsize=18)
ax3.spines['right'].set_position(('outward', 80))
ax3.spines['right'].set_color('red')
x3 = Cadmium_Pon['Time (min)']
y3 = Cadmium_Pon['Average_Red_Total/Total_protein']
y3error = Cadmium_Pon['Std error for Red total Trx/Total protein']
x3error = Cadmium_Pon['Std error for Red total Trx/Total protein']
ax3.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='red', capsize=3)
ax1.set_xlabel('Time (min)', color = 'Black', fontsize=18)
ax1.tick_params(axis='x', labelsize=18)
ax1.get_legend().remove()
ax2.get_legend().remove()
ax3.get_legend().remove()
plt.plot()
plt.savefig('Cadmium_triple plot.png', dpi=500, bbox_inches='tight')
In [156]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = Cadmium_Pon['Average_Red_Total/Total_protein']
y = Cadmium['Average_Red_Total/Total_Trx']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = Cadmium_Pon['Std error for Red total Trx/Total protein']
yerr = Cadmium['Std error for Trx redox charge']
plt.errorbar(x, y, xerr = Cadmium_Pon['Std error for Red total Trx/Total protein'] , yerr = Cadmium['Std error for Trx redox charge'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Trx redox charge',size=20, color='green')
plt.legend(['8 mM Cadmium sulfate'], frameon=False)
plt.xlim([0.1, 0.4])
plt.ylim([0.2, 1])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.096$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: 0.286$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: 0.238$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.009$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Ponceau_correlation_Cadmium.png', dpi=500, bbox_inches='tight')
plt.show()
In [89]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = Cadmium_Pon['Average_Red_Total/Total_protein']
y = Viability_Cadmium['Average_OD']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = Cadmium_Pon['Std error for Red total Trx/Total protein']
yerr = Viability_Cadmium['Std error of average OD']
plt.errorbar(x, y, xerr = Cadmium_Pon['Std error for Red total Trx/Total protein'], yerr = Viability_Cadmium['Std error of average OD'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='blue')
plt.legend(['8 mM Cadmium sulfate'], frameon=False)
plt.xlim([0, 0.5])
plt.ylim([0, 3])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.594$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: 0.429$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: 0.333$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.352$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Viability vs total protein_Cadmium.png', dpi=500, bbox_inches='tight')
plt.show()
In [159]:
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cyanide_Pon1.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cyanide_Pon2.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
plt.rcParams["figure.figsize"] = (16,12)
img = mpimg.imread('Cyanide_Pon3.png')
imgplot = plt.imshow(img)
plt.axis("off")
plt.show()
30 mM Potassium ferricyanide - Ponceau S (Total protein) analysis¶
In [90]:
read_file = pd.read_excel (r'C:\Users\Tejal\Desktop\Supplementary material\Cyanide Pon.xlsx')
read_file.to_csv (r'C:\Users\Tejal\Desktop\Supplementary material\Cyanide Pon.csv', index = None, header=True)
pd.read_csv(r'C:\Users\Tejal\Desktop\Supplementary material\Cyanide Pon.csv')
Cyanide_Pon = pd.read_csv('Cyanide Pon.csv')
Cyanide_Pon["R1_Red_Total/(R1_Total protein)"] = (Cyanide_Pon['R1_RedA'] + Cyanide_Pon['R1_RedB']).div(Cyanide_Pon['R1_Total_protein'])
Cyanide_Pon["R2_Red_Total/(R2_Total protein)"] = (Cyanide_Pon['R2_RedA'] + Cyanide_Pon['R2_RedB']).div(Cyanide_Pon['R2_Total_protein'])
Cyanide_Pon["R3_Red_Total/(R3_Total potein)"] = (Cyanide_Pon['R3_RedA'] + Cyanide_Pon['R3_RedB']).div(Cyanide_Pon['R3_Total_protein'])
Cyanide_Pon['Average_Red_Total/Total_protein'] = Cyanide_Pon.iloc[:, [10,11,12]].mean(axis=1)
Cyanide_Pon['Std error for Red total Trx/Total protein'] = Cyanide_Pon.iloc[:, [10,11,12]].sem(axis=1)
Cyanide_Pon
Out[90]:
In [91]:
fig, ax1 = plt.subplots()
ax2=ax1.twinx()
ax3=ax1.twinx()
ax1 = Cyanide.plot('Time (min)','Average_Red_Total/Total_Trx', color = 'green', ax=ax1, figsize=(9,6), marker = 'o')
ax1.set_ylabel('Trx redox charge', color = 'green', fontsize=18)
ax1.set_ylim(0.65,1)
ax1.tick_params(axis = 'y', colors = 'green', labelsize=18)
ax3.spines['left'].set_color('green')
x1 = Cyanide['Time (min)']
y1 = Cyanide['Average_Red_Total/Total_Trx']
y1error = Cyanide['Std error for Trx redox charge']
x1error = Cyanide['Std error for Trx redox charge']
ax1.errorbar(x1,y1,yerr=y1error,xerr=x1error, color='green', capsize=3)
ax2 = Viability_Cyanide.plot('Time (min)','Average_OD', color = 'blue', ax=ax2, marker = 'o')
ax2.set_ylabel('Absorbance (562 nm)', color = 'blue', fontsize=18)
ax2.set_ylim(0.5,2.5)
ax2.tick_params(axis = 'y', colors = 'blue', labelsize=18)
ax2.spines['right'].set_color('blue')
x2 = Viability_Cyanide['Time (min)']
y2 = Viability_Cyanide['Average_OD']
y2error = Viability_Cyanide['Std error of average OD']
x2error = Viability_Cyanide['Std error of average OD']
ax2.errorbar(x2,y2,yerr=y2error,xerr=x2error, color='blue', capsize=3)
ax3 = Cyanide_Pon.plot('Time (min)','Average_Red_Total/Total_protein', color = 'red', ax=ax3, marker = 'o')
ax3.set_ylabel('Reduced Trx/Total protein', color = 'red', fontsize=18)
ax3.set_ylim(0.1,0.5)
ax3.tick_params(axis = 'y', colors = 'red', labelsize=18)
ax3.spines['right'].set_position(('outward', 80))
ax3.spines['right'].set_color('red')
x3 = Cyanide_Pon['Time (min)']
y3 = Cyanide_Pon['Average_Red_Total/Total_protein']
y3error = Cyanide_Pon['Std error for Red total Trx/Total protein']
x3error = Cyanide_Pon['Std error for Red total Trx/Total protein']
ax3.errorbar(x3,y3,yerr=y3error,xerr=x3error, color='red', capsize=3)
ax1.set_xlabel('Time (min)', color = 'Black', fontsize=18)
ax1.tick_params(axis='x', labelsize=18)
ax1.get_legend().remove()
ax2.get_legend().remove()
ax3.get_legend().remove()
plt.plot()
plt.savefig('Cyanide_triple plot.png', dpi=500, bbox_inches='tight')
In [164]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = Cyanide_Pon['Average_Red_Total/Total_protein']
y = Cyanide['Average_Red_Total/Total_Trx']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = Cyanide_Pon['Std error for Red total Trx/Total protein']
yerr = Cyanide['Std error for Trx redox charge']
plt.errorbar(x, y, xerr = Cyanide_Pon['Std error for Red total Trx/Total protein'] , yerr = Cyanide['Std error for Trx redox charge'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Trx redox charge',size=20, color='green')
plt.legend(['30 mM Potassium ferricyanide'], frameon=False)
plt.xlim([0, 0.6])
plt.ylim([0.4, 1])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.212$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: -0.143$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: -0.143$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.045$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Ponceau_correlation_Cyanide.png', dpi=500, bbox_inches='tight')
plt.show()
In [92]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = Cyanide_Pon['Average_Red_Total/Total_protein']
y = Viability_Cyanide['Average_OD']
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
xerr = Cyanide_Pon['Std error for Red total Trx/Total protein']
yerr = Viability_Cyanide['Std error of average OD']
plt.errorbar(x, y, xerr = Cyanide_Pon['Std error for Red total Trx/Total protein'], yerr = Viability_Cyanide['Std error of average OD'], fmt='o', color="black", capsize = 4)
plt.scatter(x, y, s=40, color = 'black')
plt.xlabel(r'Reduced Trx/Total protein',size=20, color='red')
plt.ylabel(r'Absorbance (562 nm)',size=20, color='blue')
plt.legend(['30 mM Potassium ferricyanide'], frameon=False)
plt.xlim([0.1, 0.5])
plt.ylim([0, 3])
plt.xticks(fontsize=18)
plt.xticks(rotation=30)
plt.yticks(fontsize=18)
plt.figtext(0.92, 0.85, 'Pearson correlation: 0.619$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.75, 'Spearman correlation: 0.357$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.65, 'Kendall correlation: 0.238$^{ns}$', fontsize=16)
plt.figtext(0.92, 0.55, 'R-squared value: 0.383$^{ns}$', fontsize=16)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Viability vs total protein_Cyanide.png', dpi=500, bbox_inches='tight')
plt.show()
Section S7 - Supporting correlation data between the Trx redox charge and cell viability extrapolated from literature¶
Data were extrapolated from Figures 2D and 3C.
Zou, L., Lu, J., Wang, J., Ren, X., Zhang, L., Gao, Y., Rottenberg, M. E. & Holmgren, A. 2017. Synergistic antibacterial effect of silver and ebselen against multidrug‐resistant Gram‐negative bacterial infections. EMBO Mol Med, 9, 1165-1178.
In [22]:
from numpy.random import randn
from numpy.random import seed
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy import stats
plt.figure(figsize=(10,7))
x = [79.72972973, 79.72972973, 70.4954955, 44.36936937, 40.54054054, 42.56756757, 23.64864865, 28.6036036]
y = [19.42, 26.99, 41.36, 51.07, 54.17, 67.38, 66.80, 64.27]
yerr = [0.970873786, 6.796116505, 4.27184466, 0.970873786, 3.300970874, 4.466019417, 1.941747573, 3.883495146]
corr, pvalue = scipy.stats.pearsonr(x, y)
print('Pearson correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.spearmanr(x, y)
print('Spearman correlation coefficient:', corr)
print('P-value:', pvalue)
corr, pvalue = scipy.stats.kendalltau(x, y)
print('Kendall correlation coefficient:', corr)
print('P-value:', pvalue)
slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y)
print('R-squared value:',r_value**2)
print('P-value:', p_value)
print('Slope:', slope)
print('Intercept:', intercept)
print('Std error:', std_err)
print('Distance correlation, P-value')
dcor, pval = distance_corr(x, y, seed=None)
print(round(dcor, 3), pval)
plt.scatter(x, y, s=40, color = 'black', label = 'Ebselen + Silver')
plt.xlabel(r'Trx redox charge (%)',size=20)
plt.ylabel(r'Cell death (%)',size=20)
plt.legend(['Ebselen + Silver'], frameon=False)
plt.errorbar(x,y,yerr=yerr, fmt=' ', color='black', capsize=4)
plt.legend(fontsize=18)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
# ns = non-significant, * = p<0.05, ** = p<0.01, *** = p<0.001, **** = p<0.0001
plt.savefig('Ebselen.png', dpi=500, bbox_inches='tight')
plt.show()
Section S8 - Model of hydrogen peroxide metabolism in fission yeast¶
A computational model of hydrogen peroxide metabolism was used to assess the stability of the thioredoxin redox charge in response to the external hydrogen peroxide concentrations tested in this study (100, 300, 500 and 1250 $\mu$M).
Tomalin, L. E., Day, A. M., Underwood, Z. E., Smith, G. R., Dalle Pezze, P., Rallis, C., Patel, W., Dickinson, B. C., Bähler, J., Brewer, T. F., Chang, C. J.-L., Shanley, D. P. & Veal, E. A. 2016. Increasing extracellular H2O2 produces a bi-phasic response in intracellular H2O2, with peroxiredoxin hyperoxidation only triggered once the cellular H2O2-buffering capacity is overwhelmed. Free Radical Biology & Medicine, 95, 333-348.
In [2]:
%matplotlib inline
from matplotlib import pyplot as plt
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter)
import numpy as np
import scipy as sp
import os
backupdir = os.getcwd()
import pysces
import time
import numpy
import copy
pysces.PyscesModel.MODEL_DIR=backupdir
pysces.PyscesModel.OUTPUT_DIR=backupdir
os.chdir(backupdir)
8.1 No stress control¶
In [4]:
o=pysces.model('Tomalin.psc')
o.H2O2_ex_init = 0
o.doSim(points = 1001, end = 1000)
sim = o.sim[5:] # get rid of initial burst
t = sim.Time
red_rate = sim.reaction_13*1e6
SH = sim.Trx1SH
SS = sim.Trx1_ox
RP = (-0.27+(0.0615/2)*numpy.log10(SS/SH))*1000
RR = SH/SS
oxRC = SS/(SH+SS)
redRC = SH/(SH+SS)
fig=plt.figure(figsize=(10,7))
ax1 = fig.add_subplot()
ax1.plot(t, red_rate, 'k-' )
ax1.set_ylabel(r'Rate (pM/s)', size=26)
ax1.set_xlabel(r'Time (s)', size=26)
ax1.set_xlim([0, 1000])
ax1.set_ylim([0, 10.5])
ax2 = ax1.twinx()
ax2.plot(t, redRC, 'b-')
ax2.set_ylabel(r'Trx Redox Charge', size=26, color='b')
ax2.set_yticks(np.arange(0.992, 1, 0.001))
ax1.tick_params(axis = 'y', labelsize=26)
ax2.tick_params(axis = 'y', labelsize=26)
ax1.tick_params(axis = 'x', labelsize=26)
ax1.tick_params(axis = 'x', rotation=30)
fig.tight_layout()
fig.savefig('No H2O2.png', dpi=500)
8.2 Hydrogen peroxide (100 $\mu$M)¶
In [3]:
o=pysces.model('Tomalin.psc')
o.H2O2_ex_init = 100.0
o.doSim(points = 1001, end = 1000)
sim = o.sim[5:] # get rid of initial burst
t = sim.Time
red_rate = sim.reaction_13*1e6
SH = sim.Trx1SH
SS = sim.Trx1_ox
RP = (-0.27+(0.0615/2)*numpy.log10(SS/SH))*1000
RR = SH/SS
oxRC = SS/(SH+SS)
redRC = SH/(SH+SS)
fig=plt.figure(figsize=(10,7))
ax1 = fig.add_subplot()
ax1.plot(t, red_rate, 'k-' )
ax1.set_ylabel(r'Rate (pM/s)', size=26)
ax1.set_xlabel(r'Time (s)', size=26)
ax1.set_xlim([0, 1000])
ax1.set_ylim([0, 10.5])
ax2 = ax1.twinx()
ax2.plot(t, redRC, 'b-')
ax2.set_ylabel(r'Trx Redox Charge', size=26, color='b')
ax2.set_yticks(np.arange(0.992, 1, 0.001))
ax1.tick_params(axis = 'y', labelsize=26)
ax2.tick_params(axis = 'y', labelsize=26)
ax1.tick_params(axis = 'x', labelsize=26)
ax1.tick_params(axis = 'x', rotation=30)
fig.tight_layout()
fig.savefig('100 uM H2O2.png', dpi=500)
8.3 Hydrogen peroxide (300 $\mu$M)¶
In [5]:
o=pysces.model('Tomalin.psc')
o.H2O2_ex_init = 300.0
o.doSim(points = 1001, end = 1000)
sim = o.sim[5:] # get rid of initial burst
t = sim.Time
red_rate = sim.reaction_13*1e6
SH = sim.Trx1SH
SS = sim.Trx1_ox
RP = (-0.27+(0.0615/2)*numpy.log10(SS/SH))*1000
RR = SH/SS
oxRC = SS/(SH+SS)
redRC = SH/(SH+SS)
fig=plt.figure(figsize=(10,7))
ax1 = fig.add_subplot()
ax1.plot(t, red_rate, 'k-' )
ax1.set_ylabel(r'Rate (pM/s)', size=26)
ax1.set_xlabel(r'Time (s)', size=26)
ax1.set_xlim([0, 1000])
ax1.set_ylim([0, 10.5])
ax2 = ax1.twinx()
ax2.plot(t, redRC, 'b-')
ax2.set_ylabel(r'Trx Redox Charge', size=26, color='b')
ax2.set_yticks(np.arange(0.992, 1, 0.001))
ax1.tick_params(axis = 'y', labelsize=26)
ax2.tick_params(axis = 'y', labelsize=26)
ax1.tick_params(axis = 'x', labelsize=26)
ax1.tick_params(axis = 'x', rotation=30)
fig.tight_layout()
fig.savefig('300 uM H2O2.png', dpi=500)
8.4 Hydrogen peroxide (500 $\mu$M)¶
In [6]:
o=pysces.model('Tomalin.psc')
o.H2O2_ex_init = 500.0
o.doSim(points = 1001, end = 1000)
sim = o.sim[5:] # get rid of initial burst
t = sim.Time
red_rate = sim.reaction_13*1e6
SH = sim.Trx1SH
SS = sim.Trx1_ox
RP = (-0.27+(0.0615/2)*numpy.log10(SS/SH))*1000
RR = SH/SS
oxRC = SS/(SH+SS)
redRC = SH/(SH+SS)
fig=plt.figure(figsize=(10,7))
ax1 = fig.add_subplot()
ax1.plot(t, red_rate, 'k-' )
ax1.set_ylabel(r'Rate (pM/s)', size=26)
ax1.set_xlabel(r'Time (s)', size=26)
ax1.set_xlim([0, 1000])
ax1.set_ylim([0, 10.5])
ax2 = ax1.twinx()
ax2.plot(t, redRC, 'b-')
ax2.set_ylabel(r'Trx Redox Charge', size=26, color='b')
ax2.set_yticks(np.arange(0.992, 1, 0.001))
ax1.tick_params(axis = 'y', labelsize=26)
ax2.tick_params(axis = 'y', labelsize=26)
ax1.tick_params(axis = 'x', labelsize=26)
ax1.tick_params(axis = 'x', rotation=30)
fig.tight_layout()
fig.savefig('500 uM H2O2.png', dpi=500)
8.5 Hydrogen peroxide (1250 $\mu$M)¶
In [7]:
o=pysces.model('Tomalin.psc')
o.H2O2_ex_init = 1250.0
o.doSim(points = 1001, end = 1000)
sim = o.sim[5:] # get rid of initial burst
t = sim.Time
red_rate = sim.reaction_13*1e6
SH = sim.Trx1SH
SS = sim.Trx1_ox
RP = (-0.27+(0.0615/2)*numpy.log10(SS/SH))*1000
RR = SH/SS
oxRC = SS/(SH+SS)
redRC = SH/(SH+SS)
fig=plt.figure(figsize=(10,7))
ax1 = fig.add_subplot()
ax1.plot(t, red_rate, 'k-' )
ax1.set_ylabel(r'Rate (pM/s)', size=26)
ax1.set_xlabel(r'Time (s)', size=26)
ax1.set_xlim([0, 1000])
ax1.set_ylim([0, 10.5])
ax2 = ax1.twinx()
ax2.plot(t, redRC, 'b-')
ax2.set_ylabel(r'Trx Redox Charge', size=26, color='b')
ax2.set_yticks(np.arange(0.992, 1, 0.001))
ax1.tick_params(axis = 'y', labelsize=26)
ax2.tick_params(axis = 'y', labelsize=26)
ax1.tick_params(axis = 'x', labelsize=26)
ax1.tick_params(axis = 'x', rotation=30)
fig.tight_layout()
fig.savefig('1250 uM H2O2.png', dpi=500)
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